互联网医院发展历程，AI原生互联网医院定义，技术发展（交互方式，数据采集，分析方式，主动被动，全程服务），定义，发展现状

互联网医疗不是一个法律概念，我国现行的医事法律制度主要围绕诊疗活动及其参与方的主体资质开展规制，本文聚焦互联网医疗商业场景中患者端（C端）围绕诊疗活动这一核心合法要素展开的几种商业模式：互联网医疗平台提供的在线健康咨询问诊、互联网医院提供的互联网诊疗活动、部分适用于诊疗场景的医疗人工智能（AI）产品。本文概述3类商业模式的发展现况；分析其发展中的主要问题，包括在线问诊行为的法律属性认定、互联网医院发展的困境、样本数据量不足对医疗AI发展的影响、监管难对互联网医疗发展的反向制约等。互联网医疗的进一步发展需要在保证医疗安全的前提下进一步放宽互联网诊疗范围，这需要在监管体系进一步完善的基础上，须构建切实可行的互联网医疗风险预警与监督管理机制。

自2015年全国第一家互联网医院诞生至今，政府先后出台了以《互联网诊疗管理办法(试行)》为代表的多项政策给予支持。在政策先行的背后，当前的行业发展仍然面临着诊疗范围的双重限制、患者信息保护的缺失和医患参与率较低等现实困境。而陷入困境的根本原因在于我国互联网医疗领域顶层设计的搭建尚未跟进行业发展的节奏，相关立法缺位和制度冲突亦阻碍了行业的发展。为此，我们应当在梳理现行政策及结合行业发展现状的基础上，以明确和适度扩大诊疗范围、细化监管制度入手对现行政策先予完善，再从健全医生多点执业制度、开展患者个人信息保护等方面多角度加强互联网医疗全领域的立法构建，从而为行业的持续发展提供全面而坚实的制度保障。

互联网医疗作为医疗产业发展的新兴业态，既具有互联网行业的内生普遍风险，又具有规制覆盖滞后的盲区与外生风险。随着互联网信息技术不断进步，线上诊疗在当前初步发展的基础上会逐渐融合并重构传统医疗服务过程。在医学发展过程中，新技术的出现普遍带有风险特征，因此互联网医疗的监管需秉持包容审慎的理念，兼顾培育市场与规范发展，构建规制共同体，从合法性、合规性、合理性和优胜性等4个维度协同治理，以法律为底线，充分运用在线预警监测、非现场监管、声誉机制等多种措施，促进互联网医疗在合法合规的框架下创新发展。

作为互联网技术赋能医患交互的重要场景，互联网诊疗具备促进优质医疗资源均衡配置的应用价值，但其服务体系当前在我国仍未能形成可持续发展的生态闭环。在人民群众医疗服务需求日趋增长的当下，我国的互联网诊疗服务体系需构建深入基层的大健康产业链，实现从单一被动诊断治疗到全面主动健康管理的数智化转型。本文基于文献回顾和理论推演方法研究发现，这一转型的实现有赖于宏观政策层面、中观功能层面和微观技术层面的共同驱动，并且互联网诊疗的内部生态与外部环境的共生演进也是促使其转型取得成功的关键。在内部生态层面，实现多元服务主体间的协同是维持互联网诊疗可持续发展的核心要义，其中数智赋权和数智赋能是实现互联网诊疗数智化转型的两大路径。具体建议包括：建立以基层为中心的数字医共体，以此为主体深耕核心诊疗业务、开展远程联合门诊；同时丰富服务层级，搭建涵盖“诊前—诊中—诊后”闭环服务的主动健康管理全流程服务链，为患者提供全面的连续性诊疗服务；建立合理的双边激励机制，满足多元主体的利益诉求，实现医患之间的激励相容。在外部环境层面，需要从政治、经济、社会和技术方面打造友好互联网诊疗的外部环境，为互联网诊疗的数智化转型提供条件机遇与外驱动力。

数智技术的快速发展为医疗卫生服务体系高质量发展提供了潜在的强劲动能。根据我国医疗卫生服务体系的现状，结合习近平总书记对新质生产力的相关论述以及党的二十届三中全会的指引，医疗卫生服务体系高质量发展应围绕以“云、大、隐、区、物、移、智”为代表的数智技术，驱动要素优化、服务优化和治理优化为发展机制，聚焦“发展方式”的高效内涵式提升、“供给模式”的优质连续性整合，以及“管理手段”的科学现代化转型。本文将分析数智驱动下的“要素优化—服务优化—治理优化”发展机制，并提出相应的实施路径，为全面构建中国特色的优质高效医疗卫生服务体系提供参考。

在健康中国战略深入推进的背景下，医疗装备的整合创新与系统转化成为提升医疗卫生服务能力、突破“卡脖子”技术的关键路径。该文针对当前医疗装备创新中存在的资源分散、协同低效、转化壁垒等问题，构建以“医研产学用”深度融合为核心的整合创新机制，设计覆盖“需求识别—研发验证—注册审批—市场应用”的全流程系统转化模式。通过多主体协同平台建设、大数据与人工智能赋能、政策保障体系优化等策略，实现创新资源的高效配置与转化流程的动态优化。研究结合联影医疗、迈瑞医疗等典型案例，验证了整合创新对缩短研发周期、提升转化效率的显著作用，为加速高端医疗装备国产化、增强全球竞争力提供系统性解决方案。

背景复旦大学附属儿科医院基于前期自行研发的智能辅助诊断工具，融合本地化部署的DeepSeek大模型，构建智慧医疗系统，以提升儿科医疗服务效率和医患满意度。 目的评价已构建的智慧医疗系统在儿科医院患儿全流程医疗服务真实场景中的应用效果。 设计横断面调查。 方法结合医学知识库、知识图谱及检索增强生成等技术，在原有“小布AI医生”基础上，构建覆盖诊前、诊中、诊后全流程的儿科智慧医疗系统（简称DS-小布医生 2.0），通过医院大数据平台采集性能指标，并分别以随机数字表法抽取50名门诊医生、以便利抽样法选取200名患儿家长，进行可用性评估。 主要结局指标系统的总体性能及诊前、诊中和诊后评价指标。 结果DS-小布医生2.0于2025年3月3日至5月11日临床应用期间，系统服务11 957人次患儿，累计使用86 533次。核心性能表现为：峰值CPU利用率5%，推理链路完成时间5.9 s，医学推理准确率81.5%。全流程各阶段指标优异：诊前导诊建议使用率82.3%；诊中诊断准确率92.4%，信息提取准确率96.4%；诊后患儿随访依从率74.0%。该系统的准确率为92.4%，双语评估替补（4元语法）指标（BLEU-4）评分为0.87，面向召回的摘要评估指标（最长公共子序列）（ROUGE-L）评分为0.73，交叉熵指标（CEM）为0.92，以上指标均优于美国OPEN AI公司的GPT-4 Med模型和来自美国斯坦福大学的Bio MedLM模型。在用户可用性评价方面，50名医生完成测试后系统可用性问卷（PSSUQ）调查，在系统质量、信息质量、界面质量和总体评价方面可用性较好；采用净推荐值量表（NPS）对200名患儿家长进行调查，净推荐值达+78分。 结论DS-小布医生2.0实现了高精度的儿科全流程智慧医疗服务，用户满意度高，为缓解儿科医疗资源短缺问题提供了有效的技术方案。

算力服务是数字中国建设的重要基础和支撑，是提升国家数字化能力和核心竞争力的关键因素。在数字中国背景下，算力服务体系需要适应不同领域、层次和场景的算力需求，实现算力资源的合理配置和高效利用，引领数字经济的发展和创新。本文基于全球视角和我国现状，厘清了算力服务的内涵，剖析了我国算力服务发展中存在的供需矛盾突出、算力基础资源分布不均匀、资源流通途径尚未建立、技术服务标准不统一等痛点问题，从算力服务形态基础、演进模型、顶层设计三个方面提出了我国算力服务体系建设的总体架构，并全面阐述了我国算力服务体系重点战略和发展路径。研究建议：加强算力顶层设计，推进算网融合发展；优化算力资源布局，降低算力使用门槛；搭建算力共享平台，盘活社会算力价值；健全算力利用机制，建立算力租赁制度；激发科技攻关潜力，发挥算力人才优势。

针对未来万物智联时代网络架构的需求，提出了新型云原生网络架构，即因云而生、为云而存、依云而建。同时提出了采用网络孪生的概念和机制来解决云原生网络中移动性、安全性和可用性等挑战。阐述了云原生网络架构设计的3个基本问题，即编址编号、映射索引和资源调度。最后，探讨了基于网络孪生的云原生网络的6G应用场景及未来研究方向。

随着数字孪生、工业物联网、边缘智能与元宇宙等虚实技术的快速演进，虚实融合已成为推动智能社会构建与产业体系重塑的核心驱动力。算力作为虚实融合的底层支撑要素，正在从单一集中式计算资源向多层协同、智能调度与安全可信的复杂系统加速演化。本文系统梳理了新型算力体系的发展现状与关键特征，指出当前算力体系正呈现“云 ‒ 边 ‒ 端”一体化演进的趋势，智能算力正成为算力结构升级的核心引擎，区域算力布局逐步形成了差异化与协同并重的格局，虚实融合驱动下的算力应用模式亦呈现出多样化、泛在化与自主化的特征；在虚实融合应用背景下，进一步分析了支撑新型算力体系构建的关键技术，包括虚实融合驱动的算力体系架构设计、面向虚实融合场景的关键技术要素，从体系架构与算力编排两方面揭示了算力供需匹配的逻辑基础；通过对混合计算架构的研究，重点探讨了虚实融合的算力体系在异构协同、低延迟高带宽保障、多源数据安全与隐私保护等方面面临的挑战；针对上述挑战，提出了构建泛在智能算网、发展可信算力体系、突破异构协同壁垒、完善安全治理机制与培育虚实算力生态等新型算力体系发展重点方向，为未来虚实算力体系的建设、产业生态优化以及算力资源配置策略提供理论参考与战略支撑。

加快智慧主动健康服务的创新应用是新时代推进健康中国建设的重要构成，也是后疫情时期满足民众健康需求的有效举措。本文围绕构建智慧主动健康服务新范式，以提高主动健康干预和管理能力、为民众提供高质量健康服务为主旨，分析了主动健康服务的发展现状与困境，概括了其智慧化发展趋势；阐述了智慧主动健康服务的概念、内涵，并基于结构化分析方法，凝练了智慧主动健康服务的技术体系；构建了智慧主动健康服务“一个中心、一个门户、三个端点”的应用框架并提出了内外协同的生态系统；从技术集成和智能应用维度总结了智慧主动健康服务的应用场景与实践案例。研究建议：强化宏观政策工具，改善自身发展环境；提高民众数字素养，重塑服务参与氛围；构建服务标准体系，筑牢内部数字生态；打造多元供给格局，持续增进服务质量；加快“产学研用”融合，提升科技成果转化，以推进我国智慧主动健康服务的可持续、高质量发展。

近年来，数字孪生作为新兴的人工智能技术为精准医疗的发展前景提供了更多可能性。数字孪生技术可以综合利用物体的全方位数据信息构建虚拟实体，通过在实体与虚拟体之间构建动态连接，提高模型分类、预测的准确性。目前，数字孪生在精准医疗领域的应用不仅包括治疗难度较大的专科疾病，也包括全生命周期、全人群层面的健康管理。但这些应用大多停留于技术模型的设计及利用单中心数据的验证层面，更多潜在的应用价值有待开发。本文对数字孪生在精准医疗应用中的研究进展和挑战进行归纳综述，为进一步突破技术瓶颈、拓宽应用领域、加快应用落地、强化法律法规提供思路与方向。

The rapid evolution of modern lifestyles has led to increasing public demand for proactive health interventions and the effective management of major chronic diseases. Traditional health management approaches have demonstrated limitations in addressing diverse and complex health needs， whereas digital therapeutics powered by multimodal large language models present transformative opportunities for advancing intervention strategies in chronic disease care. By integrating big data， artificial intelligence， and other cutting-edge information technologies， digital therapeutics deliver personalized， remote， and data-driven solutions that enhance the prevention， monitoring， and management of major chronic conditions. These innovations not only improve clinical outcomes but also optimize the allocation of healthcare resources and elevate patients′ quality of life. Multimodal large language models， acting as "digital health assistants，" enable intelligent integration of text and visual data to support medical image interpretation， real-time health monitoring， and conversational clinical decision-making for both clinicians and patients. This paper aims to investigate the integration of multimodal large language models into digital therapeutics for proactive health management， examining their applications， current domestic and international research advancements， existing challenges， and future development trends， thereby offering theoretical insights and practical guidance for the advancement of proactive healthcare systems.

Agentic AI systems are increasingly capable of autonomous data science workflows, yet clinical prediction tasks demand domain expertise that purely automated approaches struggle to provide. We investigate how human guidance of agentic AI can improve multimodal clinical prediction, presenting our approach to all three AgentDS Healthcare benchmark challenges: 30-day hospital readmission prediction (Macro-F1 = 0.8986), emergency department cost forecasting (MAE = $465.13), and discharge readiness assessment (Macro-F1 = 0.7939). Across these tasks, human analysts directed the agentic workflow at key decision points, multimodal feature engineering from clinical notes, scanned PDF billing receipts, and time-series vital signs; task-appropriate model selection; and clinically informed validation strategies. Our approach ranked 5th overall in the healthcare domain, with a 3rd-place finish on the discharge readiness task. Ablation studies reveal that human-guided decisions compounded to a cumulative gain of +0.065 F1 over automated baselines, with multimodal feature extraction contributing the largest single improvement (+0.041 F1). We distill three generalizable lessons: (1) domain-informed feature engineering at each pipeline stage yields compounding gains that outperform extensive automated search; (2) multimodal data integration requires task-specific human judgment that no single extraction strategy generalizes across clinical text, PDFs, and time-series; and (3) deliberate ensemble diversity with clinically motivated model configurations outperforms random hyperparameter search. These findings offer practical guidance for teams deploying agentic AI in healthcare settings where interpretability, reproducibility, and clinical validity are essential.

The emergence of agentic AI marks a new phase in the digital transformation of healthcare. Distinct from conventional generative AI, agentic AI systems are capable of autonomous, goal-directed actions and complex task coordination. They promise to support or even collaborate with clinicians and patients in increasingly independent ways. While agentic AI raises familiar moral concerns regarding safety, accountability, and bias, this article focuses on a less explored dimension: its capacity to transform the moral fabric of healthcare itself. Drawing on the framework of techno-moral change and the three domains of decision, relation and perception, we investigate how agentic AI might reshape the patient-physician relationship and reconfigure core concepts of medical morality. We argue that these shifts, while not fully predictable, demand ethical attention before widespread deployment. Ultimately, the paper calls for integrating ethical foresight into the design and use of agentic AI.

Large language models (LLMs) show promise for healthcare question answering, but clinical use is limited by weak verification, insufficient evidence grounding, and unreliable confidence signalling. We propose a multi-agent medical QA framework that combines complementary LLMs with evidence retrieval, uncertainty estimation, and bias checks to improve answer reliability. Our approach has two phases. First, we fine-tune three representative LLM families (GPT, LLaMA, and DeepSeek R1) on MedQuAD-derived medical QA data (20k+ question-answer pairs across multiple NIH domains) and benchmark generation quality. DeepSeek R1 achieves the strongest scores (ROUGE-1 0.536 +- 0.04; ROUGE-2 0.226 +-0.03; BLEU 0.098 -+ 0.018) and substantially outperforms the specialised biomedical baseline BioGPT in zero-shot evaluation. Second, we implement a modular multi-agent pipeline in which a Clinical Reasoning agent (fine-tuned LLaMA) produces structured explanations, an Evidence Retrieval agent queries PubMed to ground responses in recent literature, and a Refinement agent (DeepSeek R1) improves clarity and factual consistency; an optional human validation path is triggered for high-risk or high-uncertainty cases. Safety mechanisms include Monte Carlo dropout and perplexity-based uncertainty scoring, plus lexical and sentiment-based bias detection supported by LIME/SHAP-based analyses. In evaluation, the full system achieves 87% accuracy with relevance around 0.80, and evidence augmentation reduces uncertainty (perplexity 4.13) compared to base responses, with mean end-to-end latency of 36.5 seconds under the reported configuration. Overall, the results indicate that agent specialisation and verification layers can mitigate key single-model limitations and provide a practical, extensible design for evidence-based and bias-aware medical AI.

Objective: This paper introduces a patient simulator for scalable, automated evaluation of healthcare conversational agents, generating realistic, controllable interactions that systematically vary across medical, linguistic, and behavioral dimensions to support risk assessment across populations. Methods: Grounded in the NIST AI Risk Management Framework, the simulator integrates three profile components: (1) medical profiles constructed from All of Us electronic health records using risk-ratio gating; (2) linguistic profiles modeling health literacy and condition-specific communication; and (3) behavioral profiles representing cooperative, distracted, and adversarial engagement. Profiles were evaluated against NIST AI RMF trustworthiness requirements and assessed against an AI Decision Aid for antidepressant selection. Results: Across 500 simulated conversations, the simulator revealed monotonic degradation in AI Decision Aid performance across health literacy levels: Rank-1 concept retrieval ranged from 47.6% (limited) to 81.9% (proficient), with corresponding recommendation degradation. Medical concept fidelity was high (96.6% across 8,210 concepts), validated by human annotators (0.73 kappa) and an LLM judge with comparable agreement (0.78 kappa). Behavioral profiles were reliably distinguished (0.93 kappa), and linguistic profiles showed moderate agreement (0.61 kappa). Conclusions: The simulator exposes measurable performance risks in conversational healthcare AI. Health literacy emerged as a primary risk factor with direct implications for equitable AI deployment.

Across healthcare, agentic artificial intelligence (AI) systems are increasingly promoted as capable of autonomous action, yet in practice they currently operate under near-total human oversight due to safety, regulatory, and liability constraints that make autonomous clinical reasoning infeasible in high-stakes environments. While market enthusiasm suggests a revolution in healthcare agents, the conceptual assumptions and accountability structures shaping these systems remain underexamined. We present a qualitative study based on interviews with 20 stakeholders, including developers, implementers, and end users. Our analysis identifies three mutually reinforcing tensions: conceptual fragmentation regarding the definition of `agentic'; an autonomy contradiction where commercial promises exceed operational reality; and an evaluation blind spot that prioritizes technical benchmarks over sociotechnical safety. We argue that agentic {AI} functions as a site of contested meaning-making where technical aspirations, commercial incentives, and clinical constraints intersect, carrying material consequences for patient safety and the distribution of blame.

Large language models (LLMs) have enabled a new class of agentic AI systems that reason, plan, and act by invoking external tools. However, most existing agentic architectures remain centralized and monolithic, limiting scalability, specialization, and interoperability. This paper proposes a framework for scalable agentic intelligence, termed the Internet of Agentic AI, in which autonomous, heterogeneous agents distributed across cloud and edge infrastructure dynamically form coalitions to execute task-driven workflows. We formalize a network-native model of agentic collaboration and introduce an incentive-compatible workflow-coalition feasibility framework that integrates capability coverage, network locality, and economic implementability. To enable scalable coordination, we formulate a minimum-effort coalition selection problem and propose a decentralized coalition formation algorithm. The proposed framework can operate as a coordination layer above the Model Context Protocol (MCP). A healthcare case study demonstrates how domain specialization, cloud-edge heterogeneity, and dynamic coalition formation enable scalable, resilient, and economically viable agentic workflows. This work lays the foundation for principled coordination and scalability in the emerging era of Internet of Agentic AI.

The integration of Artificial Intelligence (AI) into clinical settings presents a software engineering challenge, demanding a shift from isolated models to robust, governable, and reliable systems. However, brittle, prototype-derived architectures often plague industrial applications and a lack of systemic oversight, creating a ``responsibility vacuum''where safety and accountability are compromised. This paper presents an industry case study of the ``Maria''platform, a production-grade AI system in primary healthcare that addresses this gap. Our central hypothesis is that trustworthy clinical AI is achieved through the holistic integration of four foundational engineering pillars. We present a synergistic architecture that combines Clean Architecture for maintainability with an Event-driven architecture for resilience and auditability. We introduce the Agent as the primary unit of modularity, each possessing its own autonomous MLOps lifecycle. Finally, we show how a Human-in-the-Loop governance model is technically integrated not merely as a safety check, but as a critical, event-driven data source for continuous improvement. We present the platform as a reference architecture, offering practical lessons for engineers building maintainable, scalable, and accountable AI-enabled systems in high-stakes domains.

Healthcare organizations are beginning to embed agentic AI into routine workflows, including clinical documentation support and early-warning monitoring. As these capabilities diffuse across departments and vendors, health systems face agent sprawl, causing duplicated agents, unclear accountability, inconsistent controls, and tool permissions that persist beyond the original use case. Existing AI governance frameworks emphasize lifecycle risk management but provide limited guidance for the day-to-day operations of agent fleets. We propose a Unified Agent Lifecycle Management (UALM) blueprint derived from a rapid, practice-oriented synthesis of governance standards, agent security literature, and healthcare compliance requirements. UALM maps recurring gaps onto five control-plane layers: (1) an identity and persona registry, (2) orchestration and cross-domain mediation, (3) PHI-bounded context and memory, (4) runtime policy enforcement with kill-switch triggers, and (5) lifecycle management and decommissioning linked to credential revocation and audit logging. A companion maturity model supports staged adoption. UALM offers healthcare CIOs, CISOs, and clinical leaders an implementable pattern for audit-ready oversight that preserves local innovation and enables safer scaling across clinical and administrative domains.

Large Language Models (LLMs) are increasingly applied in healthcare, yet ensuring their ethical integrity and safety compliance remains a major barrier to clinical deployment. This work introduces a multi-agent refinement framework designed to enhance the safety and reliability of medical LLMs through structured, iterative alignment. Our system combines two generative models - DeepSeek R1 and Med-PaLM - with two evaluation agents, LLaMA 3.1 and Phi-4, which assess responses using the American Medical Association's (AMA) Principles of Medical Ethics and a five-tier Safety Risk Assessment (SRA-5) protocol. We evaluate performance across 900 clinically diverse queries spanning nine ethical domains, measuring convergence efficiency, ethical violation reduction, and domain-specific risk behavior. Results demonstrate that DeepSeek R1 achieves faster convergence (mean 2.34 vs. 2.67 iterations), while Med-PaLM shows superior handling of privacy-sensitive scenarios. The iterative multi-agent loop achieved an 89% reduction in ethical violations and a 92% risk downgrade rate, underscoring the effectiveness of our approach. This study presents a scalable, regulator-aligned, and cost-efficient paradigm for governing medical AI safety.

AI agents are increasingly used in long, multi-turn workflows in both research and enterprise settings. As interactions grow, agent behavior often degrades due to loss of constraint focus, error accumulation, and memory-induced drift. This problem is especially visible in real-world deployments where context evolves, distractions are introduced, and decisions must remain consistent over time. A common practice is to equip agents with persistent memory through transcript replay or retrieval-based mechanisms. While convenient, these approaches introduce unbounded context growth and are vulnerable to noisy recall and memory poisoning, leading to unstable behavior and increased drift. In this work, we introduce the Agent Cognitive Compressor (ACC), a bio-inspired memory controller that replaces transcript replay with a bounded internal state updated online at each turn. ACC separates artifact recall from state commitment, enabling stable conditioning while preventing unverified content from becoming persistent memory. We evaluate ACC using an agent-judge-driven live evaluation framework that measures both task outcomes and memory-driven anomalies across extended interactions. Across scenarios spanning IT operations, cybersecurity response, and healthcare workflows, ACC consistently maintains bounded memory and exhibits more stable multi-turn behavior, with significantly lower hallucination and drift than transcript replay and retrieval-based agents. These results show that cognitive compression provides a practical and effective foundation for reliable memory control in long-horizon AI agents.

Reliable clinical decision support requires medical AI agents capable of safe, multi-step reasoning over structured electronic health records (EHRs). While large language models (LLMs) show promise in healthcare, existing benchmarks inadequately assess performance on action-based tasks involving threshold evaluation, temporal aggregation, and conditional logic. We introduce ART, an Action-based Reasoning clinical Task benchmark for medical AI agents, which mines real-world EHR data to create challenging tasks targeting known reasoning weaknesses. Through analysis of existing benchmarks, we identify three dominant error categories: retrieval failures, aggregation errors, and conditional logic misjudgments. Our four-stage pipeline -- scenario identification, task generation, quality audit, and evaluation -- produces diverse, clinically validated tasks grounded in real patient data. Evaluating GPT-4o-mini and Claude 3.5 Sonnet on 600 tasks shows near-perfect retrieval after prompt refinement, but substantial gaps in aggregation (28--64%) and threshold reasoning (32--38%). By exposing failure modes in action-oriented EHR reasoning, ART advances toward more reliable clinical agents, an essential step for AI systems that reduce cognitive load and administrative burden, supporting workforce capacity in high-demand care settings

Systematic reviews are essential for evidence-based medicine, but reviewing 1.5 million+ annual publications manually is infeasible. Current AI approaches suffer from hallucinations in systematic review tasks, with studies reporting rates ranging from 28--40% for earlier models to 2--15% for modern implementations which is unacceptable when errors impact patient care. We present a causal graph-enhanced retrieval-augmented generation system integrating explicit causal reasoning with dual-level knowledge graphs. Our approach enforces evidence-first protocols where every causal claim traces to retrieved literature and automatically generates directed acyclic graphs visualizing intervention-outcome pathways. Evaluation on 234 dementia exercise abstracts shows CausalAgent achieves 95% accuracy, 100% retrieval success, and zero hallucinations versus 34% accuracy and 10% hallucinations for baseline AI. Automatic causal graphs enable explicit mechanism modeling, visual synthesis, and enhanced interpretability. While this proof-of-concept evaluation used ten questions focused on dementia exercise research, the architectural approach demonstrates transferable principles for trustworthy medical AI and causal reasoning's potential for high-stakes healthcare.

Digital twins, as precise digital representations of physical systems, have evolved from passive simulation tools into intelligent and autonomous entities through the integration of artificial intelligence technologies. This paper presents a unified four-stage framework that systematically characterizes AI integration across the digital twin lifecycle, spanning modeling, mirroring, intervention, and autonomous management. By synthesizing existing technologies and practices, we distill a unified four-stage framework that systematically characterizes how AI methodologies are embedded across the digital twin lifecycle: (1) modeling the physical twin through physics-based and physics-informed AI approaches, (2) mirroring the physical system into a digital twin with real-time synchronization, (3) intervening in the physical twin through predictive modeling, anomaly detection, and optimization strategies, and (4) achieving autonomous management through large language models, foundation models, and intelligent agents. We analyze the synergy between physics-based modeling and data-driven learning, highlighting the shift from traditional numerical solvers to physics-informed and foundation models for physical systems. Furthermore, we examine how generative AI technologies, including large language models and generative world models, transform digital twins into proactive and self-improving cognitive systems capable of reasoning, communication, and creative scenario generation. Through a cross-domain review spanning eleven application domains, including healthcare, aerospace, smart manufacturing, robotics, and smart cities, we identify common challenges related to scalability, explainability, and trustworthiness, and outline directions for responsible AI-driven digital twin systems.

The proliferation of Internet of things (IoT) devices in smart cities, transportation, healthcare, and industrial applications, coupled with the explosive growth of AI-driven services, has increased demands for efficient distributed computing architectures and networks, driving cloud-edge-terminal collaborative intelligence (CETCI) as a fundamental paradigm within the artificial intelligence of things (AIoT) community. With advancements in deep learning, large language models (LLMs), and edge computing, CETCI has made significant progress with emerging AIoT applications, moving beyond isolated layer optimization to deployable collaborative intelligence systems for AIoT (CISAIOT), a practical research focus in AI, distributed computing, and communications. This survey describes foundational architectures, enabling technologies, and scenarios of CETCI paradigms, offering a tutorial-style review for CISAIOT beginners. We systematically analyze architectural components spanning cloud, edge, and terminal layers, examining core technologies including network virtualization, container orchestration, and software-defined networking, while presenting categorizations of collaboration paradigms that cover task offloading, resource allocation, and optimization across heterogeneous infrastructures. Furthermore, we explain intelligent collaboration learning frameworks by reviewing advances in federated learning, distributed deep learning, edge-cloud model evolution, and reinforcement learning-based methods. Finally, we discuss challenges (e.g., scalability, heterogeneity, interoperability) and future trends (e.g., 6G+, agents, quantum computing, digital twin), highlighting how integration of distributed computing and communication can address open issues and guide development of robust, efficient, and secure collaborative AIoT systems.

Digital twins (DT) are increasingly used in healthcare to model patients, processes, and physiological systems. While recent solutions leverage visualization, visual analytics, and user interaction, these systems rarely incorporate structured service design methodologies. Bridging service design with visual analytics and visualization can be valuable for the healthcare DT community. This paper aims to introduce the service design discipline to visualization researchers by framing this integration gap and suggesting research directions to enhance the real-world applicability of DT solutions.

With rapid digitization and digitalization, drawing a fine line between the digital and the physical world has become nearly impossible. It has become essential more than ever to integrate all spheres of life into a single Digital Thread to address pressing challenges of modern society: accessible and inclusive healthcare in terms of equality and equity. Techno-social advancements and mutual acceptance have enabled the infusion of digital models to simulate social settings with minimum resource utilization to make effective decisions. However, a significant gap exists in feeding back the models with appropriate real-time changes. In other words, active behavioral modeling of modern society is lacking, influencing community healthcare as a whole. By creating virtual replicas of (physical) behavioral systems, digital twins can enable real-time monitoring, simulation, and optimization of urban dynamics. This paper explores the potential of digital twins to promote inclusive healthcare for evolving smart cities. We argue that digital twins can be used to: Identify and address disparities in access to healthcare services, Facilitate community participation, Simulate the impact of urban policies and interventions on different groups of people, and Aid policy-making bodies for better access to healthcare. This paper proposes several ways to use digital twins to stitch the actual and virtual societies. Several discussed concepts within this framework envision an active, integrated, and synchronized community aware of data privacy and security. The proposal also provides high-level step-wise transitions that will enable this transformation.

Digital twin (DT) is the recurrent and common feature in discussions about future technologies, bringing together advanced communication, computation, and artificial intelligence, to name a few. In the context of Industry 4.0, industries such as manufacturing, automotive, and healthcare are rapidly adopting DT-based development. The main challenges to date have been the high demands on communication and computing resources, as well as privacy and security concerns, arising from the large volumes of data exchanges. To achieve low latency and high security services in the emerging DT, multi-tier computing has been proposed by combining edge/fog computing and cloud computing. Specifically, low latency data transmission, efficient resource allocation, and validated security strategies of multi-tier computing systems are used to solve the operational problems of the DT system. In this paper, we introduce the architecture and applications of DT using examples from manufacturing, the Internet-of-Vehicles and healthcare. At the same time, the architecture and technology of multi-tier computing systems are studied to support DT. This paper will provide valuable reference and guidance for the theory, algorithms, and applications in collaborative multi-tier computing and DT.

The absence of pre-hospital physiological data in standard clinical datasets fundamentally constrains the early prediction of stroke, as patients typically present only after stroke has occurred, leaving the predictive value of continuous monitoring signals such as photoplethysmography (PPG) unvalidated. In this work, we overcome this limitation by focusing on a rare but clinically critical cohort - patients who suffered stroke during hospitalization while already under continuous monitoring - thereby enabling the first large-scale analysis of pre-stroke PPG waveforms aligned to verified onset times. Using MIMIC-III and MC-MED, we develop an LLM-assisted data mining pipeline to extract precise in-hospital stroke onset timestamps from unstructured clinical notes, followed by physician validation, identifying 176 patients (MIMIC) and 158 patients (MC-MED) with high-quality synchronized pre-onset PPG data, respectively. We then extract hemodynamic features from PPG and employ a ResNet-1D model to predict impending stroke across multiple early-warning horizons. The model achieves F1-scores of 0.7956, 0.8759, and 0.9406 at 4, 5, and 6 hours prior to onset on MIMIC-III, and, without re-tuning, reaches 0.9256, 0.9595, and 0.9888 on MC-MED for the same horizons. These results provide the first empirical evidence from real-world clinical data that PPG contains predictive signatures of stroke several hours before onset, demonstrating that passively acquired physiological signals can support reliable early warning, supporting a shift from post-event stroke recognition to proactive, physiology-based surveillance that may materially improve patient outcomes in routine clinical care.

For patients undergoing systemic cancer therapy, the time between clinic visits is full of uncertainties and risks of unmonitored side effects. To bridge this gap in care, we developed and prospectively trialed a multi-modal AI framework for remote patient monitoring (RPM). This system integrates multi-modal data from the HALO-X platform, such as demographics, wearable sensors, daily surveys, and clinical events. Our observational trial is one of the largest of its kind and has collected over 2.1 million data points (6,080 patient-days) of monitoring from 84 patients. We developed and adapted a multi-modal AI model to handle the asynchronous and incomplete nature of real-world RPM data, forecasting a continuous risk of future adverse events. The model achieved an accuracy of 83.9% (AUROC=0.70). Notably, the model identified previous treatments, wellness check-ins, and daily maximum heart rate as key predictive features. A case study demonstrated the model's ability to provide early warnings by outputting escalating risk profiles prior to the event. This work establishes the feasibility of multi-modal AI RPM for cancer care and offers a path toward more proactive patient support.(Accepted at Europe NeurIPS 2025 Multimodal Representation Learning for Healthcare Workshop. Best Paper Poster Award.)

Building on decades of success in digital infrastructure and biomedical innovation, we propose the Personal Care Utility (PCU) - a cybernetic system for lifelong health guidance. PCU is conceived as a global, AI-powered utility that continuously orchestrates multimodal data, knowledge, and services to assist individuals and populations alike. Drawing on multimodal agents, event-centric modeling, and contextual inference, it offers three essential capabilities: (1) trusted health information tailored to the individual, (2) proactive health navigation and behavior guidance, and (3) ongoing interpretation of recovery and treatment response after medical events. Unlike conventional episodic care, PCU functions as an ambient, adaptive companion - observing, interpreting, and guiding health in real time across daily life. By integrating personal sensing, experiential computing, and population-level analytics, PCU promises not only improved outcomes for individuals but also a new substrate for public health and scientific discovery. We describe the architecture, design principles, and implementation challenges of this emerging paradigm.

Personalized chronic care requires the integration of multimodal health data to enable precise, adaptive, and preventive decision-making. Yet most current digital twin (DT) applications remain organ-specific or tied to isolated data types, lacking a unified and privacy-preserving foundation. This paper introduces the Patient Medical Digital Twin (PMDT), an ontology-driven in silico patient framework that integrates physiological, psychosocial, behavioral, and genomic information into a coherent, extensible model. Implemented in OWL 2.0, the PMDT ensures semantic interoperability, supports automated reasoning, and enables reuse across diverse clinical contexts. Its ontology is structured around modular Blueprints (patient, disease and diagnosis, treatment and follow-up, trajectories, safety, pathways, and adverse events), formalized through dedicated conceptual views. These were iteratively refined and validated through expert workshops, questionnaires, and a pilot study in the EU H2020 QUALITOP project with real-world immunotherapy patients. Evaluation confirmed ontology coverage, reasoning correctness, usability, and GDPR compliance. Results demonstrate the PMDT's ability to unify heterogeneous data, operationalize competency questions, and support descriptive, predictive, and prescriptive analytics in a federated, privacy-preserving manner. By bridging gaps in data fragmentation and semantic standardization, the PMDT provides a validated foundation for next-generation digital health ecosystems, transforming chronic care toward proactive, continuously optimized, and equitable management.

Cardiovascular diseases (CVDs) remain the foremost cause of mortality worldwide, a burden worsened by a severe deficit of healthcare workers. Artificial intelligence (AI) agents have shown potential to alleviate this gap through automated detection and proactive screening, yet their clinical application remains limited by: 1) rigid sequential workflows, whereas clinical care often requires adaptive reasoning that select specific tests and, based on their results, guides personalised next steps; 2) reliance solely on intrinsic model capabilities to perform role assignment without domain-specific tool support; 3) general and static knowledge bases without continuous learning capability; and 4) fixed unimodal or bimodal inputs and lack of on-demand visual outputs when clinicians require visual clarification. In response, a multimodal framework, CardAIc-Agents, was proposed to augment models with external tools and adaptively support diverse cardiac tasks. First, a CardiacRAG agent generated task-aware plans from updatable cardiac knowledge, while the Chief agent integrated tools to autonomously execute these plans and deliver decisions. Second, to enable adaptive and case-specific customization, a stepwise update strategy was developed to dynamically refine plans based on preceding execution results, once the task was assessed as complex. Third, a multidisciplinary discussion team was proposed which was automatically invoked to interpret challenging cases, thereby supporting further adaptation. In addition, visual review panels were provided to assist validation when clinicians raised concerns. Experiments across three datasets showed the efficiency of CardAIc-Agents compared to mainstream Vision-Language Models (VLMs) and state-of-the-art agentic systems.

Agitation is one of the most common responsive behaviors in people living with dementia, particularly among those residing in community settings without continuous clinical supervision. Timely prediction of agitation can enable early intervention, reduce caregiver burden, and improve the quality of life for both patients and caregivers. This study aimed to develop and benchmark machine learning approaches for the early prediction of agitation in community-dwelling older adults with dementia using multimodal sensor data. A new set of agitation-related contextual features derived from activity data was introduced and employed for agitation prediction. A wide range of machine learning and deep learning models was evaluated across multiple problem formulations, including binary classification for single-timestamp tabular sensor data and multi-timestamp sequential sensor data, as well as anomaly detection for single-timestamp tabular sensor data. The study utilized the Technology Integrated Health Management (TIHM) dataset, the largest publicly available dataset for remote monitoring of people living with dementia, comprising 2,803 days of in-home activity, physiology, and sleep data. The most effective setting involved binary classification of sensor data using the current 6-hour timestamp to predict agitation at the subsequent timestamp. Incorporating additional information, such as time of day and agitation history, further improved model performance, with the highest AUC-ROC of 0.9720 and AUC-PR of 0.4320 achieved by the light gradient boosting machine. This work presents the first comprehensive benchmarking of state-of-the-art techniques for agitation prediction in community-based dementia care using privacy-preserving sensor data. The approach enables accurate, explainable, and efficient agitation prediction, supporting proactive dementia care and aging in place.

Digital twins (DTs) are redefining healthcare by paving the way for more personalized, proactive, and intelligent medical interventions. As the shift toward personalized care intensifies, there is a growing need for an individual's virtual replica that delivers the right treatment at the optimal time and in the most effective manner. The emerging concept of a Human Digital Twin (HDT) holds the potential to revolutionize the traditional healthcare system much like digital twins have transformed manufacturing and aviation. An HDT mirrors the physical entity of a human body through a dynamic virtual model that continuously reflects changes in molecular, physiological, emotional, and lifestyle factors. This digital representation not only supports remote monitoring, diagnosis, and prescription but also facilitates surgery, rehabilitation, and overall personalized care, thereby relieving pressure on conventional healthcare frameworks. Despite its promising advantages, there are considerable research challenges to overcome as HDT technology evolves. In this study, I will initially delineate the distinctions between traditional digital twins and HDTs, followed by an exploration of the networking architecture integral to their operation--from data acquisition and communication to computation, management, and decision-making--thereby offering insights into how these innovations may reshape the modern healthcare industry.

… and different service needs of AI-native healthcare systems. In … allocation approach for AI-native healthcare systems in 6G … coverage areas common in hospital environments. Building …

… demands an integrated, AI–native architecture spanning Critical Access Hospitals (CAHs) and rural water utilities. Despite near–universal basic EHR adoption in hospitals, only a …

With the introduction of the 6 G networks, it is possible to have ultra-reliable and low-latency healthcare monitoring and smart task-oriented communication. The paper proposes an AI-friendly semantic communication system of predictive healthcare monitoring based on hierarchical edge intelligence. The system does not send the raw physiological signals to the receiver, but it derives task-relevant semantic features of the biomedical data which are collected in PhysioNet datasets and only sends to the receiver the clinically meaningful representations. An implementation of a transformer-based time-series model is known as an early anomaly detector and risk scorer on wearable devices and multi-tier edge nodes, and semantic compression of data. The recommended framework consuames less bandwidth and less energy but still has high diagnostic accuracy. Network slicing and adaptive semantic encoding is combined to prioritize the events that are critical to health in the dynamic 6 G environment. The experimental analysis shows that the occurrence of latency reduction and communication efficiency are considerably enhanced when applied in contrast to the traditional cloud-centric solutions. The architecture proposed is capable of supporting the next generation smart medical system of scalable, privacy-conscious and real-time predictive healthcare applications.

The adoption of Artificial Intelligence (AI) in healthcare has moved beyond experimental decision-support systems to operational infrastructures, of which hospitals and healthcare facilities operate on a mission-critical level, impacting diagnostics, treatment planning, care coordination, and hospital administration. Nevertheless, interoperability difficulties, operational fragmentation, wireless network variability, lack of trust as well as insufficient adaptive decision capability construction limit large-scale healthcare application of machine learning algorithms and data-driven analytics, although the progress has been significant. This paper is an in-depth guideline to converting AI into scalable healthcare provision by means of adaptive decision-making skills and wireless-conscious system gummy. The proposed solution focuses on AI-native infrastructure, dynamic resource-management, explainable decision-modeling, and optimizing behavior with the help of the network. We view healthcare AI systems as cyber-physical decision ecosystems, where sensing, computation, communication and clinical action are closely intertwined. In contrast to conventional AI architectures, where the models are known to run in disconnected cloud infrastructures, contemporary healthcare delivery requires low-latency edge computing, real-time fused data across diverse medical devices and the ability to cope with wireless variability in hospital, rural and telehealth settings. Thus, we propose the idea of wirelessly-aware system intelligence (WASI), whereby AI systems dynamically change inference pipelines, model compression plans and data-routing policies in response to the state of the network, latency, and bandwidth. Such wireless-conscious solution guarantees continuity of care especially in remote monitoring and emergency triage conditions. The approach incorporates adaptability of decision capability engineering, operative AI lifecycle management, federated learning framework, and trustful explainable AI modules. It proposes a multi-layer model including: sensing layer, wireless communication layer, edge intelligence layer, cloud orchestration layer and governance layer. Mathematical models are offered to characterize adaptive decision optimization to be modeled under latency and reliability. The outcomes of the simulations prove better system throughput, a decrease in latency by 32 percent, and more efficiency in care coordination coupled with improved patient outcome measures over deployments that did not respond to patients by AI. The findings confirm the paramount role of harmonizing AI models with components of wireless infrastructure consciousness, adaptive scaling models, and moral regulatory systems. In addition, the paper also assesses the preparedness to deploy at tertiary hospitals, rural telemedicine, and urban digital health ecosystems. The suggested framework can provide the ordered route to AI-native healthcare change in 2025 and further.

… hospital beds and hundreds of millions of patient interactions annually when calculated against China’s total hospital … The March 2026 launch of the world’s first AI-native medical facility …

Modern applications increasingly rely on knowledge and collective wisdom. While the internet offers abundant sources, it is untrustworthy, incomplete, and essentially a vast, heterogeneous database lacking current information. With innovation accelerating exponentially, harnessing collective intelligence is essential to drive progress. We present Consensus Without Authority, a general, ledger-backed meta-protocol framework that transforms any group of self-interested actors into a trusted collective-intelligence engine. Each actor i produces a local insight $L{I_i} = {f_1}\left( {\mathcal{Q},{\theta _i}} \right)$, evaluates its peers Ui = f2({LIj}), and enters a two-phase commit-reveal cycle. Harmonizers aggregate votes V = f3({Ui}), synthesize candidate knowledge CKj = f4(V,{LIi}), and a smart contract finalizes each round r by CK(r) = f5({CKj}). We argue that, under majority-honest assumptions and token-weighted incentives, honest play is a Nash equilibrium and the framework converges to a unique fixed point even in Byzantine settings.While first applied to federated learning, the framework is not limited to distributed ML training. It supports socially scoped cognition, where actors contribute local knowledge and reasoning to a shared space, enriching common knowledge iteratively.Two case studies illustrate its range: (i) crowd-sourced RLHF, where dispersed annotators guide policy updates without sharing labels; (ii) federated LoRA tuning of large language models across siloed hospitals, achieving near-centralized accuracy under HIPAA and GDPR.We place the framework within an emerging "AI-native stack": a Byzantine ledger for immutable state, Google’s A2A for agent-to-agent messaging, and Anthropic’s MCP for secure tool access — together enabling a Cognitive Internet where autonomous agents learn, trade, and verify knowledge without centralized trust, creating a scalable, interoperable substrate for next-generation human–AI collaboration.

… Artificial Intelligence (AI) is transforming the healthcare … This paper explores the emergence of next generation "agentic AI" … These systems enhance various aspects of healthcare, …

The rapid advancement of large language models (LLMs) has accelerated the development of agentic AI. This paradigm carries significant implications for healthcare, an ecosystem characterized by its knowledge intensity and complex decision-making requirements. At the same time, the high-stakes and safety-critical nature of healthcare poses unique challenges, making general-purpose agentic frameworks often inadequate. The autonomy that defines these agents introduces additional concerns regarding trust, reliability, and alignment with clinical constraints. With research in this area growing exponentially over the past two years, this comprehensive survey provides a systematic map, analyzing over 200 recent studies. We propose a holistic taxonomy that traces the full lifecycle of healthcare agents, beginning with the Perception of diverse clinical modalities. From there, it examines the core Agentic Capabilities and Architectures that enable autonomous action. We then map the Application Ecosystem by organizing use cases around the key stakeholders they serve (clinicians, patients, researchers, and administrators) and conclude with a review of Evaluation frameworks. Furthermore, we discuss the limitations of existing methods from diverse perspectives and systematically propose future research directions. To facilitate ongoing research, a curated list of all related papers is available at https://github.com/AgenticHealthAI/Awesome-Agentic-AI-for-Healthcare/.

Introduction Rapid advancements in artificial intelligence (AI) have ushered in an era of hyperautomation and intelligent orchestration across multiple engineering domains, with healthcare emerging as one of the most impactful application areas. Among recent developments, Agentic AI has gained attention as a sub-domain of AI capable of autonomous operation, decision-making, and goal-driven behavior with minimal human intervention. This study aims to explore the architectural and functional role of Agentic AI in modern healthcare systems. Methods The study adopts a conceptual and analytical approach to examine the core components of Agentic AI, including agent design, decision-making mechanisms, task allocation strategies, agent coordination, and ranking frameworks. It further investigates the integration of emerging 6G networking technologies within Agentic AI architectures. A qualitative case study on remote robotic surgery is presented to illustrate practical applicability. Additionally, a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis is conducted to assess strategic and operational considerations. Results The analysis demonstrates that Agentic AI architectures, when supported by high-speed and low-latency 6G communication, can enable efficient autonomous decision-making and coordinated task execution in complex healthcare workflows. The case study highlights the feasibility of Agentic AI in enabling remote robotic surgery with enhanced responsiveness, precision, and reliability. The SWOT analysis reveals strong potential for scalability and efficiency while also identifying challenges related to ethical governance, system robustness, and security. Discussion The findings suggest that Agentic AI represents a promising paradigm for next-generation healthcare systems, particularly in remote and critical care applications. While the proposed framework offers architectural insights and strategic value, responsible integration requires addressing limitations such as trust, regulatory compliance, and system transparency. Overall, this study provides a holistic understanding of how Agentic AI can be effectively and ethically integrated into healthcare ecosystems.

Background/ Objectives: Agentic AI represents a promising evolution of AI technology applied to healthcare, with systems increasingly capable of operating autonomously to achieve defined clinical goals. However, the literature lacks conceptual clarity between “AI agents” and “agentic AI”, and few studies have rigorously explored their clinical applications. Therefore, this study aims to conduct a novel systematic review addressing this gap by examining agentic AI systems in healthcare settings, characterizing their applications, features, outcomes, and limitations, and clarifying the conceptual distinctions between AI agents and agentic AI systems using predefined and objective criteria. Methods A comprehensive search was conducted across PubMed, Embase, Cochrane, Scopus, and Google Scholar on April 6th, 2025. Studies were included if they involved AI systems in healthcare settings that demonstrated the following agentic features: autonomous operation, goal-directed behavior, and initiating action. Data on the clinical tasks achieved by the agents, key findings, features, and limitations were collected from the included studies. Screening and extraction followed PRISMA guidelines, with Risk of Bias assessed using ROBINS-I and Cochrane's Risk of Bias tools. Results Of 984 retrieved records, seven studies met the inclusion criteria, spanning domains such as emergency medicine, oncology, radiology, and rehabilitation. Multi-agent architecture was frequently used to decompose and coordinate complex workflows. Among the included studies, the AIs showed high accuracy in diagnosing cancer patients, conducting treatment plans, sending alerts, coaching messages, analysing image data, and adapting to challenging experimental scenarios. While demonstrating potential for improved efficiency, task accuracy, and patient engagement, significant limitations were noted: narrow task scope, lack of physical agency, limited clinical validation, and barriers to integration into real-world healthcare systems. Only one system had been deployed in a patient-facing trial setting. Conclusion The current literature suggests an emerging role and application of Agentic AI, holding promise with the potential to revolutionize diagnostics, triage, treatment planning, and patient management. However, real-world implementation and evaluations in the literature are limited. Future research must address critical validation, regulation, ethics, and clinical integration challenges to realize their full potential. Clear operational definitions and frameworks for evaluating agency are essential to support safe and effective deployment of these systems.

The advancement of artificial intelligence (AI) in healthcare has opened new avenues for improving patient out-comes through enhanced diagnostic accuracy, optimized treatment strategies, and more efficient clinical workflows. This study presents a novel Agentic AI system designed to address key inefficiencies in modern healthcare systems. Unlike conventional AI approaches, the proposed system integrates adaptive decision-making mechanisms that dynamically respond to patient conditions, ensuring real-time anomaly detection, predictive diagnostics, and intelligent intervention strategies. To validate its effectiveness, the model was tested across three critical healthcare scenarios. The early disease detection module achieved a predictive accuracy of 92.3%, outperforming traditional risk models. The real-time patient monitoring system demonstrated an anomaly detection rate of 98.7%, with an alert response time of just 2.5 seconds, showcasing its reliability in clinical settings. Moreover, the proposed system outperformed existing AI solutions, attaining a 96.5% confidence score, reducing misdiagnosis rates by 22%, and surpassing traditional radiologists in both sensitivity and specificity. This work bridges the gap between theoretical AI advancements and real-world healthcare applications, laying the foundation for intelligent, autonomous systems in modern medical practice.

… As AI continues to evolve, particularly in health care, we are witnessing the rise of software … services through intelligent digital agents, marking the beginning of the age of agentic AI. …

Generative AI (GenAI) and Agentic AI are considered the most important state-of-the-art approaches that are shaping the future of healthcare solutions. This systematic literature review (SLR) examines the capabilities & applications as well as clinical benefits for both Generative AI (GenAI) and the Agentic AI in healthcare. This review started with the identification of 87 works from Scopus and PubMed databases. After applying eligibility criteria, duplicates removal, and full-text screening, a total of 18 records published between 2024 and 2025 were included for the final study. The review revealed how Generative AI and Agentic AI are being applied across key areas in healthcare. GenAI is mainly used for diagnostics, automated documentation, and simulation-based training, while Agentic AI supports autonomous tasks and interactive multilingual tools. The findings show that successful AI adoption depends not just on technology, but also on thoughtful workflow design, clinician expertise, organizational readiness, and system integration. Thematic opportunities emerged in workflow optimization, clinical decision support, training and knowledge management, and patient engagement. Although research on complex and goal-directed Agentic AI applications is still limited, these AI approaches offer significant promise to enhance healthcare delivery and guide practical, responsible implementation.

… This paper aims to investigate the use of agentic AI in healthcare, specifically … healthcare professionals to provide a comprehensive understanding of the current state of AI in healthcare. …

… By treating agentic AI as an augmentation to human-driven healthcare rather than as a replacement for it, this work outlines a pragmatic path forward. Agentic systems offer a means to …

… the emerging landscape of AI agents in healthcare, from … It provides a taxonomy of agentic systems by autonomy level, … the promise of agentic AI to transform healthcare delivery and the …

Background: Agentic AI systems are increasingly proposed for healthcare applications, yet the evidence base distinguishing computational promise from clinical reality remains poorly characterised. Single-agent systems offer efficiency for routine diagnostics; multi-agent systems promise robustness for complex care. Both face barriers in safety, accountability, and equitable deployment. Methods: We conducted a PRISMA-ScR scoping review synthesising evidence from 161 studies (January 2018–October 2024, with selective early-access coverage through April 2026) retrieved from PubMed, IEEE Xplore, arXiv, Google Scholar, and Scopus. Evidence certainty was graded using an adapted GRADEinformed framework appropriate for heterogeneous clinical and simulation evidence. Given substantial heterogeneity across architectures, tasks, and outcome measures, quantitative pooling was not appropriate; we employed structured evidence mapping and narrative synthesis. A pragmatic, deployment-focused definition of “agent” was adopted and extended with a five-level Agentic Capability Spectrum (Levels 0–4) to preserve discriminative power. Results: High-certainty evidence supports selected single-agent systems in specialised diagnostic domains (94.5% accuracy in retinal screening; AUC 0.96 in skin-cancer classification). Very low-certainty evidence from simulation studies suggests potential coordination advantages for multi-agent systems, with no confirmed clinical deployment. Multi-agent systems require substantially higher computational resources and introduce coordination latency (200–500 ms in simulation). Cross-cutting barriers include algorithmic bias in one commercial population-health algorithm (Moderate-certainty; Obermeyer et al., 2019; generalisability uncertain), unclear liability frameworks, and workflow-integration failures. Evidence is predominantly from high-income countries (87% of studies; descriptive evidence-mapping finding). Conclusions: Single-agent systems demonstrate validated clinical utility in constrained tasks, whereas multiagent systems remain experimental. Priorities include large-scale clinical trials for multi-agent architectures, standardised safety frameworks, risk-based regulatory pathways, and equity-focused global deployment strategies. As this synthesis was conducted by a single reviewer, all findings represent a preliminary structured synthesis requiring independent replication before informing clinical guideline development.

This chapter explores the transformative role of AI agents in healthcare, detailing their impact across clinical decision support, diagnostics, drug discovery, administrative workflows, …

… deep into Agentic AI, the basics as well as its application toward revolutionizing health care. … , this chapter will elucidate how Agentic AI is changing the healthcare industry-better, more …

… areas of future research within agentic AI: a need for complex predictive models with a higher level of detail, ethical compatibility of agentic AI with current healthcare contexts, and the …

Agentic artificial intelligence (AI) represents a pivotal shift in clinical decision support, moving beyond static tools by reasoning, adapting, and acting alongside clinicians. Psychiatry, grounded in subjective experience, trust, and longitudinal care, offers both an opportunity and a high-stakes testbed. Agentic systems may enhance documentation, personalize care, support continuous monitoring, and extend access, while raising risks around bias, explainability, privacy, and therapeutic alliance. In this Perspective, we (i) define psychiatry-specific agentic AI distinct from decision-support and fully autonomous systems; (ii) synthesize current evidence across studies; (iii) propose assistive, collaborative, and semi-autonomous roles; and (iv) outline a roadmap for responsible implementation.

Agentic Artificial Intelligence (Agentic-AI) represents a new frontier in the design of intelligent systems capable of autonomous, adaptive, and secure decision-making in dynamic healthcare environments. With the healthcare industry facing unprecedented challenges-from managing chronic diseases to ensuring rapid emergency response-there is a pressing need for scalable, accurate, and real-time solutions. This paper introduces a novel Agentic AI framework that integrates real-time anomaly detection, predictive diagnostics, and secure clinical decisionmaking to improve patient outcomes. We validated the system across three high-impact clinical scenarios. For early disease detection, the model achieved a 92.3% prediction accuracy using historical patient records. In real-time patient monitoring, the agentic module reached a 98.7% anomaly detection rate with a 2.5-second alert response time, proving critical for emergency interventions. Finally, the diagnostic support system demonstrated a 96.5% confidence score in detecting pulmonary anomalies, reducing misdiagnoses by 22% compared to human and existing AI baselines. The dataset was curated from reputable sources including MIMIC-III for electronic health records, PAMAP2 for wearable sensor data, and TCIA for medical imaging. This work bridges the gap between theoretical AI innovation and real-world deployment by introducing a clinically relevant, autonomous AI solution. The proposed system sets a new benchmark for intelligent and secure healthcare by providing timely, accurate, and explainable outcomes, paving the way for next-generation clinical decision support systems.

Over the last few decades, our digitally expanding world has experienced another significant digitalization boost because of the COVID-19 pandemic. Digital transformations are changing every aspect of this world. New technological innovations are springing up continuously, attracting increasing attention and investments. Digital twin, one of the highest trending technologies of recent years, is now joining forces with the healthcare sector, which has been under the spotlight since the outbreak of COVID-19. This paper sets out to promote a better understanding of digital twin technology, clarify some common misconceptions, and review the current trajectory of digital twin applications in healthcare. Furthermore, the functionalities of the digital twin in different life stages are summarized in the context of a digital twin model in healthcare. Following the Internet of Things as a service concept and digital twining as a service model supporting Industry 4.0, we propose a paradigm of digital twinning everything as a healthcare service, and different groups of physical entities are also clarified for clear reference of digital twin architecture in healthcare. This research discusses the value of digital twin technology in healthcare, as well as current challenges and insights for future research.

Recent trends have shown a widespread increase in the landscape of digital healthcare (i.e., Healthcare 4.0) services, such as personalized healthcare, intelligent rehabilitation, telemedicine, and smart diet management, among others. These healthcare services are based on a variety of diverse requirements. Fulfilling these requirements require proactive intelligent analytics and self-sustainability of networks. Self-sustainability enables the operation of a network with minimum possible interaction from the end-users/network operators, whereas proactive intelligent analytics enables efficient management of resources in response to users’ requests. To enable healthcare 4.0 with proactive online analytics and self-sustainability, one can leverage digital twins. In this article, we present an overview and recent advances of digital twins for healthcare 4.0. An architecture of digital twins for healthcare is also proposed. Furthermore, we present several use cases of digital twins. Finally, we present open research challenges with possible solutions.

This article aims to make a bibliometric literature review using systematic scientific mapping and content analysis of digital twins in healthcare to know the evolution, domain, keywords, content type, and kind and purpose of digital twin's implementation in healthcare, so a consolidation and future improvement of existing knowledge can be made and gaps for new studies can be identified. The increase in publications of digital twins in healthcare is quite recent and it is still concentrated in the domain of technology sources. The subject is majorly concentrated in patient's digital twin group and in precision medicine and aspects, issues and/or policies subgroups, although the publications keywords mirror it only at the group side. Digital twins in healthcare are probably stepping out of the infancy phase. On the other hand, digital twins in hospital group and the device and facilities management subgroups are more mature with all knowledge gathered from the manufacturing sector. There is an absence of some publication's types in general, device and care subgroup and no whole body or hospital digital twin was reported. Based on the presented arguments, guidelines for future research were presented: advance in the creation of general frameworks, in subgroups not as much explored, and in groups and subgroups already explored, but that need more advancement to achieve the main goals of a whole human or hospital digital twin with the main issues resolved.

Background Although most digital twin (DT) applications for health care have emerged in precision medicine, DTs can potentially support the overall health care process. DTs (twinned systems, processes, and products) can be used to optimize flows, improve performance, improve health outcomes, and improve the experiences of patients, doctors, and other stakeholders with minimal risk. Objective This paper aims to review applications of DT systems, products, and processes as well as analyze the potential of these applications for improving health care management and the challenges associated with this emerging technology. Methods We performed a rapid review of the literature and reported available studies on DTs and their applications in health care management. We searched 5 databases for studies published between January 2002 and January 2022 and included peer-reviewed studies written in English. We excluded studies reporting DT usage to support health care practice (organ transplant, precision medicine, etc). Studies were analyzed based on their contribution toward DT technology to improve user experience in health care from human factors and systems engineering perspectives, accounting for the type of impact (product, process, or performance/system level). Challenges related to the adoption of DTs were also summarized. Results The DT-related studies aimed at managing health care systems have been growing over time from 0 studies in 2002 to 17 in 2022, with 7 published in 2021 (N=17 studies). The findings reported on applications categorized by DT type (system: n=8; process: n=5; product: n=4) and their contributions or functions. We identified 4 main functions of DTs in health care management including safety management (n=3), information management (n=2), health management and well-being promotion (n=3), and operational control (n=9). DTs used in health care systems management have the potential to avoid unintended or unexpected harm to people during the provision of health care processes. They also can help identify crisis-related threats to a system and control the impacts. In addition, DTs ensure privacy, security, and real-time information access to all stakeholders. Furthermore, they are beneficial in empowering self-care abilities by enabling health management practices and providing high system efficiency levels by ensuring that health care facilities run smoothly and offer high-quality care to every patient. Conclusions The use of DTs for health care systems management is an emerging topic. This can be seen in the limited literature supporting this technology. However, DTs are increasingly being used to ensure patient safety and well-being in an organized system. Thus, further studies aiming to address the challenges of health care systems challenges and improve their performance should investigate the potential of DT technology. In addition, such technologies should embed human factors and ergonomics principles to ensure better design and more successful impact on patient and doctor experiences.

… The main aim of this paper is to study Digital Twin and its need for the healthcare … services of Digital Twin for Healthcare. Various technologies and tools of Digital Twins for Healthcare …

Background The concept of digital twins has great potential for transforming the existing health care system by making it more personalized. As a convergence of health care, artificial intelligence, and information and communication technologies, personalized health care services that are developed under the concept of digital twins raise a myriad of ethical issues. Although some of the ethical issues are known to researchers working on digital health and personalized medicine, currently, there is no comprehensive review that maps the major ethical risks of digital twins for personalized health care services. Objective This study aims to fill the research gap by identifying the major ethical risks of digital twins for personalized health care services. We first propose a working definition for digital twins for personalized health care services to facilitate future discussions on the ethical issues related to these emerging digital health services. We then develop a process-oriented ethical map to identify the major ethical risks in each of the different data processing phases. Methods We resorted to the literature on eHealth, personalized medicine, precision medicine, and information engineering to identify potential issues and developed a process-oriented ethical map to structure the inquiry in a more systematic way. The ethical map allows us to see how each of the major ethical concerns emerges during the process of transforming raw data into valuable information. Developers of a digital twin for personalized health care service may use this map to identify ethical risks during the development stage in a more systematic way and can proactively address them. Results This paper provides a working definition of digital twins for personalized health care services by identifying 3 features that distinguish the new application from other eHealth services. On the basis of the working definition, this paper further layouts 10 major operational problems and the corresponding ethical risks. Conclusions It is challenging to address all the major ethical risks that a digital twin for a personalized health care service might encounter proactively without a conceptual map at hand. The process-oriented ethical map we propose here can assist the developers of digital twins for personalized health care services in analyzing ethical risks in a more systematic manner.

Human digital twin (HDT) is an emerging technology that can revolutionize the existing healthcare system with the ability to enable personalized healthcare services (PHS) using various tools such as artificial intelligence and blockchain. Its implementation is expected to be similar to digital twins proposed in other application areas such as manufacturing and aviation by consisting of three key dimensions: the physical entity, virtual model, and connection that characterizes the physical-virtual interactions. The known complexity of human body structure because of the constant molecular and physiological changes, however, means extracting precise medical data as well as modeling of HDT is very difficult. Hence, HDT is much more complex than its counterparts and its implementation methods remain unclear, which deserves further investigation. This article thus presents the architectural framework and key design requirements of HDT and then discusses the key technologies and technical challenges to suggest future directions. We believe that this article will open up new research opportunities and motivate new research efforts towards the development of HDT for PHS.

Since the emergence of digital and smart healthcare, the world has hastened to apply various technologies in this field to promote better health operation and patients’ well being, increase life expectancy, and reduce healthcare costs. One promising technology and game changer in this domain is digital twin (DT). DT is expected to change the concept of digital healthcare and take this field to another level that has never been seen before. DT is a virtual replica of a physical asset that reflects the current status through real-time transformed data. This article proposes and implements an intelligent context-aware healthcare system using the DT framework. This framework is a beneficial contribution to digital healthcare and to improve healthcare operations. Accordingly, an electrocardiogram (ECG) heart rhythms classifier model was built using machine learning to diagnose heart disease and detect heart problems. The implemented models successfully predicted a particular heart condition with high accuracy in different algorithms. The collected results have shown that integrating DT with the healthcare field would improve healthcare processes by bringing patients and healthcare professionals together in an intelligent, comprehensive, and scalable health ecosystem. Also, implementing an ECG classifier that detects heart conditions gives the inspiration for applying ML and artificial intelligence with different human body metrics for continuous monitoring and abnormalities detection. Finally, neural-network-based algorithms deal better with ECG data than traditional ML algorithms.

Using the PRISMA approach, we present the first systematic literature review of digital twin (DT) research in healthcare systems (HSs). This endeavor stems from the pressing need for a thorough analysis of this emerging yet fragmented research area, with the goal of consolidating knowledge to catalyze its growth. Our findings are structured around three research questions aimed at identifying: (i) current research trends, (ii) gaps, and (iii) realization challenges. Current trends indicate global interest and interdisciplinary collaborations to address complex HS challenges. However, existing research predominantly focuses on conceptualization; research on integration, verification, and implementation is nascent. Additionally, we document that a substantial body of papers mislabel their work, often disregarding modeling and twinning methods that are necessary elements of a DT. Furthermore, we provide a non-exhaustive classification of the literature based on two axes: the object (i.e., product or process) and the context (i.e., patient’s body, medical procedures, healthcare facilities, and public health). While this is a testament to the diversity of the field, it implies a specific pattern that could be reimagined. We also identify two gaps: (i) considering the human-in-the-loop nature of HSs with a focus on provider decision-making and (ii) implementation research. Lastly, we discuss two challenges for broad-scale implementation of DTs in HSs: improving virtual-to-physical connectivity and data-related issues. In conclusion, this study suggests that DT research could potentially help alleviate the acute shortcomings of HSs that are often manifested in the inability to concurrently improve the quality of care, provider wellbeing, and cost efficiency.

Digital Twin (DT) is bringing revolution to our lives by a digital representation of the physical system. DT is the creation of the joint usage of various technologies like Cyber-Physical System (CPS), Internet of Things (IoT), Big Data, Edge Computing (EC), Artificial Intelligence (AI), and Machine Learning (ML), etc. DTs are established to optimize a wide range of applications of industry, healthcare, smart cities, smart homes, etc. It is still in its early development stages. This paper fills the gaps by combining the extensive information on technologies utilized in the creation of DT in industry and healthcare. The paper focuses on studying the characteristics of DT, communication technologies and tools utilized in the creation of DT models, reference models, standards, and the researcher’s recent work in smart manufacturing and healthcare. Challenges and open issues that need attention are also discussed.

Digital twin technology is revolutionizing healthcare systems by leveraging real-time data integration, advanced analytics, and virtual simulations to enhance patient care, enable predictive analytics, optimize clinical operations, and facilitate training and simulation. With the ability to gather and analyze a wealth of patient data from various sources, digital twins can offer personalized treatment plans based on individual characteristics, medical history, and real-time physiological data. Predictive analytics and preventive interventions are made possible by machine learning algorithms, allowing for early detection of health risks and proactive interventions. Digital twins can optimize clinical operations by analyzing workflows and resource allocation, leading to streamlined processes and improved patient care. Moreover, digital twins can provide a safe and realistic environment for healthcare professionals to enhance their skills and practice complex procedures. The implementation of digital twin technology in healthcare has the potential to significantly improve patient outcomes, enhance patient safety, and drive innovation in the healthcare industry.

The digital transformation process fostered by the development of Industry 4.0 technologies has largely affected the health sector, increasing diagnostic capabilities and improving drug effectiveness and treatment delivery. The Digital Twin (DT) technology, based on the virtualization of physical assets/processes and on a bidirectional communication between the digital and physical space for data exchange, is considered a game changer in modern health systems. Digital Twin applications in healthcare are various, ranging from virtualization of hospitals' physical spaces/organizational processes to individuals' physiological/genetic/lifestyle characteristics replication, and include the modeling of public health-related processes for monitoring, optimization and planning purposes. In this paper, motivated by the current COVID-19 pandemic, we focus on the application of the Digital Twin technology for virus containment on the workplace through social distancing. The contribution of this paper is three-fold: i) we review the existing literature on the adoption of the Digital Twin technology in the healthcare domain, and propose a classification of DT applications into four categories; ii) we propose a generalized Digital Twin architecture that can be used as reference to identify the main functional components of a Digital Twin system; iii) we present CanTwin, a real-life industrial case study developed by Hitachi and representing the Digital Twin of a canteen service serving 1100 workers, set up for social distancing monitoring, queue inspection, people counting and tracking, table occupancy supervision.

Digital twins (DTs) play a vital role in revolutionising the healthcare industry, leading to more personalised, intelligent, and proactive healthcare. With the evolution of personalised healthcare, there is a significant need to represent a virtual replica for individuals to provide the right type of care in the right way and at the right time. Therefore, in this paper, we surveyed the concept of a personal digital twin (PDT) as an enhanced version of the DT with actionable insight capabilities. In particular, PDT can bring value to patients by enabling more accurate decision making and proper treatment selection and optimisation. Then, we explored the progression of PDT as a revolutionary technology in healthcare research and industry. However, although several research works have been performed for smart healthcare using DT, PDT is still at an early stage. Consequently, we believe that this work can be a step towards smart personalised healthcare industry by guiding the design of industrial personalised healthcare systems. Accordingly, we introduced a reference framework that empowers smart personalised healthcare using PDTs by bringing together existing advanced technologies (i.e., DT, blockchain, and AI). Then, we described some selected use cases, including the mitigation of COVID-19 contagion, COVID-19 survivor follow-up care, personalised COVID-19 medicine, personalised osteoporosis prevention, personalised cancer survivor follow-up care, and personalised nutrition. Finally, we identified further challenges to pave the PDT paradigm toward the smart personalised healthcare industry.

… The proposed model (Emergency Service Room with Digital Twins), helps to treat a patient with fast-track service and reduce the length of Stay in ER. The risk factor of a patient's life by …

As simulation is playing an increasingly important role in medicine, providing the individual patient with a customised diagnosis and treatment is envisaged as part of future precision medicine. Such customisation will become possible through the emergence of digital twin (DT) technology. The objective of this article is to review the progress of prominent research on DT technology in medicine and discuss the potential applications and future opportunities as well as several challenges remaining in digital healthcare. A review of the literature was conducted using PubMed, Web of Science, Google Scholar, Scopus and related bibliographic resources, in which the following terms and their derivatives were considered during the search: DT, medicine and digital health virtual healthcare. Finally, analyses of the literature yielded 465 pertinent articles, of which we selected 22 for detailed review. We summarised the application examples of DT in medicine and analysed the applications in many fields of medicine. It revealed encouraging results that DT is being increasing applied in medicine. Results from this literature review indicated that DT healthcare, as a key fusion approach of future medicine, will bring the advantages of precision diagnose and personalised treatment into reality.

This comprehensive review investigates the transformative potential of sensor-driven digital twin technology in enhancing healthcare delivery within smart environments. We explore the integration of smart environments with sensor technologies, digital health capabilities, and location-based services, focusing on their impacts on healthcare objectives and outcomes. This work analyzes the foundational technologies, encompassing the Internet of Things (IoT), Internet of Medical Things (IoMT), machine learning (ML), and artificial intelligence (AI), that underpin the functionalities within smart environments. We also examine the unique characteristics of smart homes and smart hospitals, highlighting their potential to revolutionize healthcare delivery through remote patient monitoring, telemedicine, and real-time data sharing. The review presents a novel solution framework leveraging sensor-driven digital twins to address both healthcare needs and user requirements. This framework incorporates wearable health devices, AI-driven health analytics, and a proof-of-concept digital twin application. Furthermore, we explore the role of location-based services (LBS) in smart environments, emphasizing their potential to enhance personalized healthcare interventions and emergency response capabilities. By analyzing the technical advancements in sensor technologies and digital twin applications, this review contributes valuable insights to the evolving landscape of smart environments for healthcare. We identify the opportunities and challenges associated with this emerging field and highlight the need for further research to fully realize its potential to improve healthcare delivery and patient well-being.

Healthcare systems are complex systems that need effective and efficient operations, optimizations, management, and control to offer reliable, high-quality, and cost-effective healthcare services. There are different approaches to improve the management of healthcare systems including utilizing the healthcare systems engineering principles. Healthcare systems engineering views a healthcare organization as a system and applies the engineering analysis and design principles to improve different aspects of healthcare services provided in that system. While this approach can provide many advantages for healthcare organizations, there are also many challenges hindering the ability of healthcare systems engineers from effectively accomplishing their mission. The initiation of the digital twin technology formed several potential methods for various industrial sectors to enhance their operations. Accordingly, they can help improve productivity, cost-effectiveness, reliability, quality, and flexibility. This paper studies how digital twins can be utilized for improving healthcare systems engineering processes and outcomes to enhance different aspects of healthcare systems. The paper discusses some of the challenges of healthcare systems engineering and how these challenges can be relaxed by utilizing digital twins. The paper also develops a conceptual framework to utilize digital twins for improving healthcare systems engineering processes and outcomes and discusses the prospects of such utilization on achieving the goals of healthcare systems engineering. In addition, the paper provides some discussions on the impact of this utilization and the future research and development projections of the employment of digital twins for healthcare systems engineering.

Continuous biomarker monitoring is revolutionizing chronic disease management, with glucose monitoring for diabetes as the primary example. Given the success of this approach, a transition to continuous protein monitoring (CPM, a real-time, implantable or wearable technology) could similarly advance precision medicine. In this work, we review state-of-the-art CPM platforms and their prospective clinical impact across both chronic disorders—metabolic, cardiovascular, autoimmune, and neurodegenerative—and acute crises, such as sepsis and transplant dysfunction. We also highlight remaining barriers to widespread adoption, including sensor stability, robust machine learning models for live interpretation, and responsible data handling for patient privacy. With continued engineering and clinical validation, emerging biosensor technologies could transform disease management, facilitating earlier interventions and individualizing treatment strategies, ultimately improving patient outcomes.

… a reactive approach to a proactive one. Proactive care using technology aims to prevent … overall health through early prompt detection and continuous monitoring. In this chapter, we …

Background: It is well known that 20% of the patients incur 80% of health care costs and many diseases and complications can be prevented or ameliorated with prompt intervention. One of the well-recognized strategies for cost reduction and better outcomes is to predict or identify high-risk and high-cost (HRHC) patients for proactive intervention. Objective: The objective of this study was to develop a predictive model that can be used to identify HRHC patients more accurately for proactive intervention. Methods: This is an observational study using fiscal year (FY) 2018 administrative data to predict FY 2019 total cost at the patient level. All 5,676,248 patients who received care in both FYs 2018 and 2019 from the Veterans Health Administration were included in the analyses. The Veterans Health Administration Corporate Data Warehouse was our main data source. With split-sample analyses, 3 sets of patient comorbidities and 5 statistical models were assessed for the highest predictive power. Results: The Box-Cox regression using comorbidities designated by the expanded CCSR (Clinical Classifications Software Refined) groups as predictors yielded the highest predictive power. The R 2 reached 0.51 and 0.37 for the transformed and raw scale cost, respectively. Conclusions: The predictive model developed in this study exhibits substantially higher predictive power than what has been reported in the literature. The algorithm based on administrative data and a publicly available patient classification system can be readily implemented by other value-based health systems to identify HRHC patients for proactive intervention.

Epilepsy, a neurological illness that causes repeated seizures, can interfere with everyday life and needs prompt treatment. Internet of Things (IoT) wearable Electroencephalogram (EEG) devices and Support Vector Machines (SVM) for predictive analytics are used in this study to suggest a unique strategy for proactive seizure treatment. Wearable EEG devices feed real-time brain activity data. It uses SVM, a strong machine learning method, to build a prediction model using historical EEG data to identify and predict seizures. The prediction software analyses EEG data in real-time to detect pre-seizure patterns and initiate preventive treatments. The seizure prediction method uses SVM’s capacity to handle high-dimensional data and catch complicated patterns to improve accuracy and reliability. Healthcare practitioners and caregivers may get timely warnings and react efficiently thanks to the IoT infrastructure’s seamless connectivity between wearable devices and a centralized monitoring system. It discusses the ethical and privacy issues of installing such a system, stressing user permission and data protection. Pilot investigations show promising prediction accuracy and reaction time. SVM with IoT-wearable EEG sensors for predictive seizure care offers a forward-looking technique for enhancing epilepsy patients’ quality of life by enabling individualized and proactive treatments.

This review paper explores the transformative role of data-driven initiatives in enhancing healthcare delivery and patient retention. It delves into the significant impacts of data analytics on diagnostic accuracy, predictive capabilities, operational efficiency, and patient outcomes. Additionally, the paper examines strategies for personalized patient engagement, effective patient experience management, churn prediction models, and longitudinal care. It addresses the challenges and ethical considerations related to data privacy and security, interoperability, bias and fairness, and regulatory compliance. Finally, it discusses future directions and provides recommendations for technological advancements, policy and governance, collaboration, and the sustainability and scalability of data-driven healthcare initiatives.

… AI in diabetes care promises improved health outcomes and quality of life through personalised and proactive healthcare. Future efforts should focus on continued investment, ensuring …

Preventative medicine and chronic disease management play a crucial role in reducing healthcare costs and improving long-term public health outcomes. This paper examines how early interventions, lifestyle modifications, and continuous disease management contribute to cost containment and enhanced population health. Preventative strategies, including vaccinations, screenings, and health education, mitigate the onset of chronic conditions such as diabetes, cardiovascular disease, and obesity. Additionally, proactive management approaches, such as patient-centered care, digital health monitoring, and evidence-based medical interventions, reduce hospitalizations and the financial burden associated with late-stage disease treatments. The findings indicate that investment in preventative healthcare significantly lowers long-term expenditures by minimizing emergency care reliance and reducing complications associated with unmanaged chronic illnesses. Moreover, health promotion programs and policy-driven initiatives have demonstrated positive effects on public health by encouraging early detection and adherence to treatment regimens. Countries with robust preventative care frameworks show improved health indicators, lower mortality rates, and reduced strain on healthcare systems. This paper concludes that integrating preventative medicine and chronic disease management into healthcare policies is essential for sustainable public health improvements and economic efficiency. Strengthening healthcare infrastructure, increasing accessibility to early interventions, and promoting a culture of health-conscious living are fundamental to achieving these goals. A shift from reactive to preventative healthcare models will not only enhance patient well-being but also contribute to a more resilient and cost-effective healthcare system.

Wearable technologies have emerged as powerful tools in healthcare, offering continuous monitoring and personalized insights outside traditional clinical settings. These devices have garnered significant attention in cardiovascular medicine for their potential to transform patient care and improve outcomes. This comprehensive review provides an overview of wearable technologies' evolution, advancements, and applications in cardiovascular medicine. We examine the miniaturization of sensors, integration of artificial intelligence (AI), and proliferation of remote patient monitoring solutions. Key findings include the role of wearables in the early detection of cardiovascular conditions, personalized health tracking, and remote patient management. Challenges such as data privacy concerns and regulatory hurdles are also addressed. The adoption of wearable technologies holds promise for shifting healthcare from reactive to proactive, enabling precision diagnostics, treatment optimization, and preventive strategies. Collaboration among healthcare stakeholders is essential to harnessing the full potential of wearables in cardiovascular medicine and ushering in a new era of personalized, proactive healthcare.