癌症精准治疗新闻报道中的过度乐观叙事与医患信任风险研究

目的：本研究选取在线医患交互的独有特征，从数字共情的角度探索在线医患信任的形成机制，为促进在线医患关系的良好发展提供实践指导。方法：挖掘好大夫在线医疗平台上的客观医患交互数据，构建影响在线医患信任建立因素的假设模型，采用统计软件SPSS24.0对假设进行验证。结果：研究结果表明医生共情、医患会话累计量、医生回复框平均字数和医患会话比率都能正向影响医患信任的建立，其中医生共情是最积极的影响因素。结论：医生提高医患沟通技巧、患者积极反馈、优化医疗APP功能设计、相关部门完善相关政策是促进在线医患信任建立的重要途径。

人工智能赋能医疗行业发展, 为临床诊断、治疗、康复等领域提供了精准的智能辅助, 其在医患共同决策领域亦具有较大潜力。然而, 人工智能在医患共同决策中的应用尚处于起步阶段, 新的挑战与机遇并存。因此, 本文阐述人工智能在医患共同决策中的应用现状, 探讨人工智能决策辅助在医患共同决策应用中的潜在问题与挑战, 提出可能的解决途径, 为未来人工智能决策辅助的开发与实施提供参考。

循证医学提倡医务人员应用证据并考虑患者价值和偏好作出决策。医患共同决策基于医患双方均为"专家"的理念, 即医生作为医学专家提供医学专业意见, 而患者作为了解自身偏好的专家, 双方在充分讨论后共同作出医学决策。在此过程中, 医务人员应同时具备获取最佳证据以及应用决策辅助系统实现医患共同决策的能力。本文通过比较不同医学决策模式, 讨论医患共同决策的理论与实践, 并列举与中国医疗环境相关的、医患共同决策可能面临的挑战与障碍因素, 以期为临床作出合理决策以及提高医疗服务质量提供借鉴。

在新的医学模式下, 单纯以医生为主导已不能适应现代医疗环境下的医疗服务。医患共同决策(shared decision making, SDM)的内涵是医生运用专业知识, 与患者在充分讨论治疗选择、获益与损伤等各种可能的情况下, 并考虑到患者的价值观、倾向性及处境后, 由医生与患者共同参与作出的、最适合患者个体的健康决策过程。本文就SDM的历史沿革、提倡SDM的原因、患者对SDM的需求与视角、如何在医疗实践中实施SDM进行阐述, 启示临床医生一方面需具备现代医学知识与技能, 如循证医学的方法以获得最佳临床证据; 同时还需具备以患者为中心的沟通技能, 与患者建立和谐、信任的医患关系, 了解患者对治疗的偏好, 在此基础上进行SDM, 从而达到最佳医疗照护。

循证医学仍是当今最好的医学实践模式。需要注意的是，证据本身不等于决策，决策还必须考虑现有资源和人们的价值取向。证据显示，绝大多数患者不会因使用降血压、降血脂、降血糖、抗癌药而预防重要并发症或死亡，说明现代医学的很多诊断和治疗都不精准，找到那几个为数不多的对治疗有反应的患者就成了现代医学的梦。精准医学应运而生，但它并不是新概念，也不等于孤注一掷的基因测序。精准医学依赖的大队列多因素研究由来已久，也不是新方法。医学一直在寻求精准，而且在人类认知的各个层面都有所建树，如疫苗和抗体、血型与输血、影像对病灶的定位以及白内障晶体替换手术。基因不是达到精准的唯一途径，只是提供了新的可能性。但是多数基因和疾病关联强度很低，说明基因精准指导防治的价值可能不大，利用大数据和其他预测因素是精准医学的必经之路。在使用大数据问题上，强调拥有总体、大样本、关联关系而淡化因果关系，是严重的误导。科学从来不会待考察了总体后才进行推论；研究需要的样本量恰恰与效果大小成反比；否定因果关系就是对流行病学科学原理和方法的否定，放弃了对真实性的保障，最终会导致防治的无效。因此，在确认疗效上，基于大数据的现实世界观察性结果不能取代随机对照试验的实验性证据。本文谨希望以怀疑和批评的方式，激发出精准医学和大数据蕴藏的真正潜力。

为理顺影响我国医患和谐问题的主次关系，探究对医患和谐影响力较大的因素，本文通过文献评阅，形成影响医患和谐的29类问题，按照卫生系统宏观模型建立问题层次结构模型，并进行专家论证，利用层次分析法计算问题重要性指数，并运用K-均值聚类法将问题归类，最终得出问题重要性排序。结果发现影响我国医患和谐的重要问题有7个，重要性排序第一的为"社会信任危机，医患信任缺失"，其重要性指数为0.0784，属于外部子模层。这与当前社会整体公信力下降的时代背景相吻合。因此，重建医患信任应该作为当前治理重点方向，国家应从宏观层面出发，通过制度重建社会信任。

以两本健康传播SSCI专业期刊1994—2018年刊载的文章为研究对象，采用“科学知识图谱”方法勾勒了这20多年间国际健康传播研究的历史与现状。研究发现，这20多年间健康传播的研究议题主要集中在受众研究和效果研究两个方面。其中，热点议题紧随社会现实不断转化，研究方法也不断吸纳医学方法而变得多元丰富。在研究主力方面，美国在全球研究格局中处于“一枝独秀”地位，无论是高产作者还是高产机构均由美国主导，但中国的研究者也开始崭露头角。从高被引期刊和高共被引文献来看，健康传播研究呈现出“传播学取向”和“医学取向”两种分殊。对照国际健康传播研究的演进趋势，未来我国健康传播研究的突破点包括：拓展对LGBT人口和“疑病症”患者等特殊群体的考察、关注在线健康行为实践、立足国情注重对心脑血管疾病的健康科普、重视叙事医学等人文医学传统。

【目的】 探讨融媒体时代医学学术期刊(简称医学期刊)健康科普传播的优化策略,为其有效应对伪科普,更好地履行科普使命,服务全民健康事业提供借鉴。 【方法】 在梳理医学期刊健康科普理论基础与研究框架的基础上,针对其传播的现状及挑战,引入“医学传播学”的SACRE模型,结合代表性期刊的科普实践案例,从科学性主体、科普对象、医学科普内容、传播途径、科普效果5个要素总结分析医学期刊健康科普优化的策略。 【结果】 在融媒体模式下,医学期刊的健康科普契合“医学传播学”及其SACRE模型的要求,可以基于SACRE模型,通过强调科学性主体、基于“感知疾病距离”理论细分科普对象、传播有定论的科普内容、适配健康需求场景、基于一级预防评估科普传播效果等途径进行优化,有效应对融媒体时代的挑战及需求。 【结论】 基于“医学传播学”SACRE模型的健康科普优化策略,更具权威性、科学性、系统性,可破解公众对医学科普的迫切需求与权威信息缺乏的矛盾,提升医学期刊健康传播的效能。

本研究以无糖营销中的健康焦虑为研究对象，旨在揭示糖尿病年轻化背景下健康焦虑的生成机制与治理路径。采用文献分析、个案研究与参与式观察方法，系统考察健康传播的失序逻辑与代糖产业的符号操控策略。研究发现：含糖饮料成为连接代谢危机与消费行为的关键媒介；第二，健康传播陷入"科学话语霸权"与"信息过载"的双重困境，代糖产业通过"0糖0卡"的符号操控，将健康焦虑转化为可消费的"安全幻觉"。研究提出，破解健康焦虑需超越技术治理范式，建立"生物-心理-社会"综合干预体系，在必要的健康警醒与过度的焦虑沉溺之间寻求理性平衡。

从信息加工视角探究个体对医疗信息的风险感知过程, 通过医疗信息来源、医疗信息内容与呈现形式和信息加工主体三个方面归纳其影响因素, 并基于经验-分析式加工理论和字面-要义加工理论探讨医疗信息风险感知的认知机制。今后研究可更注重医疗信息风险感知的通用研究与特殊主题研究的平衡、风险感知测量工具的规范性与特异性的结合, 并立足于中国医疗体系建立具有循证依据的预防措施和配套政策。

对于疾病的医学知识阐释常常处于黑箱之中，这与民众对病痛的巨大知识需求形成了割裂。其中，癌症的预后知识关乎患者的疾病进展和未来生活，但既有研究多将其视作基于生物医学原理的技术体系，而较少探究其生成过程和社会基础。不可否认，预后知识依托于分子层面的医学证据、基于对大量患者的跟踪调查和临床试验的资料建立了一套科学的生存指标体系来指示结果。然而，高度技术化的预后知识不仅面对着临床世界中的不确定性后果，也因受到患者的日常知识逻辑的挑战而变得高度情境化。在诊疗过程中，作为技术的预后知识往往会被临床经验和患者的日常实践所修正和取舍，呈现出从面向技术到面向患者主体的知识再建构趋势。本文基于乳腺癌疾病的诊断、治疗，勾勒出医学中的预后知识的内涵和患者对看似确定的权威知识的意义再阐释，进而提出患者群体在预后知识的生产过程中应当不断地从“被决定的生命”走向“生命自决”的位置。

… The need for optimism and hope to be sustained in the process of honestly delivering bad news and a limited life expectancy is an ideal expressed by both doctors and patients. …

… their information deliveries in a way which involved optimism, their optimism was produced in a context of diagnostic and … It can encourage patients to focus on the optimistic news and in …

Examining how family members talk through a loved one’s cancer on the telephone reveals, as a central concern, the interactional construction of hopeful and optimistic responses to uncertain and potentially despairing cancer circumstances. I refer to such recurring moments as “managing optimism”1 in talk about cancer. This chapter focuses on an initial collection of seven excerpts wherein optimism emerges as a resource for family members as they update, assimilate, and commiserate about cancer diagnosis and treatment. These materials are drawn from a set of 54 recorded and transcribed phone calls comprising the first natural history of a family talking through cancer, from Mom’s initial diagnosis until her death, some 13 months later.2 Only phone calls #1 (involving Dad and Son) and #2 (Dad, Son, and Mom) of the corpus are examined, interactions drawn from a collection of more than 100 instances where speakers engage in optimistic collaborations.

… of optimism–pessimism in relation to receiving bad news (ie, positive lymph nodes, more advanced cancer stage) after breast cancer … Overall, women’s optimism–pessimism levels …

… message content to support health care professionals in delivering less optimistic news. … physicians to deliver less optimistic messages to patients with advanced cancer include, among …

… was used to calculate their risk of breast cancer for the next 5 years and for a lifetime. Participants also completed measures of optimism and numeracy at pretest. Participants were then …

… frame emerged in the mid-1980s before some major genetic discoveries of diseases were announced (cystic fibrosis, Huntington’s disease, breast cancer) and prior to the …

… In conclusion, this study suggests that optimism about the world and television news media exposure is related. If indeed, we have created a television news media viewing population …

Background Medical-purpose software and Artificial Intelligence (“AI”)-enabled technologies (“medical AI”) raise important social, ethical, cultural, and regulatory challenges. To elucidate these important challenges, we present the findings of a qualitative study undertaken to elicit public perspectives and expectations around medical AI adoption and related sociotechnical harm. Sociotechnical harm refers to any adverse implications including, but not limited to, physical, psychological, social, and cultural impacts experienced by a person or broader society as a result of medical AI adoption. The work is intended to guide effective policy interventions to address, prioritise, and mitigate such harm. Methods Using a qualitative design approach, twenty interviews and/or long-form questionnaires were completed between September and November 2024 with UK participants to explore their perspectives, expectations, and concerns around medical AI adoption and related sociotechnical harm. An emphasis was placed on diversity and inclusion, with study participants drawn from racially, ethnically, and linguistically diverse groups and from self-identified minority groups. A thematic analysis of interview transcripts and questionnaire responses was conducted to identify general medical AI perception and sociotechnical harm. Results Our findings demonstrate that while participants are cautiously optimistic about medical AI adoption, all participants expressed concern about matters related to sociotechnical harm. This included potential harm to human autonomy, alienation and a reduction in standards of care, the lack of value alignment and integration, epistemic injustice, bias and discrimination, and issues around access and equity, explainability and transparency, and data privacy and data-related harm. While responsibility was seen to be shared, participants located responsibility for addressing sociotechnical harm primarily with the regulatory authorities. An identified concern was risk of exclusion and inequitable access on account of practical barriers such as physical limitations, technical competency, language barriers, or financial constraints. Conclusion We conclude that medical AI adoption can be better supported through identifying, prioritising, and addressing sociotechnical harm including the development of clear impact and mitigation practices, embedding pro-social values within the system, and through effective policy guidance intervention.

Background Previous benefit–risk perception studies and social experiences have clearly demonstrated that any emerging technology platform that ignores benefit–risk perception by citizens might jeopardize its public acceptability and further development. The aim of this survey was to investigate the Italian judgment on nanotechnology and which demographic and heuristic variables were most influential in shaping public perceptions of the benefits and risks of nanotechnology. Methods In this regard, we investigated the role of four demographic (age, gender, education, and religion) and one heuristic (knowledge) predisposing factors. Results The present study shows that gender, education, and knowledge (but not age and religion) influenced the Italian perception of how nanotechnology will (positively or negatively) affect some areas of everyday life in the next twenty years. Furthermore, the picture that emerged from our study is that Italian citizens, despite minimal familiarity with nanotechnology, showed optimism towards nanotechnology applications, especially those related to health and medicine (nanomedicine). The high regard for nanomedicine was tied to the perception of risks associated with environmental and societal implications (division among social classes and increased public expenses) rather than health issues. However, more highly educated people showed greater concern for health issues but this did not decrease their strong belief about the benefits that nanotechnology would bring to medical fields. Conclusion The results reported here suggest that public optimism towards nanomedicine appears to justify increased scientific effort and funding for medical applications of nanotechnology. It also obligates toxicologists, politicians, journalists, entrepreneurs, and policymakers to establish a more responsible dialog with citizens regarding the nature and implications of this emerging technology platform.

BackgroundStem cell (SC) therapies hold remarkable promise for many diseases, but there is a significant gulf between public expectations and the reality of progress toward clinical application. Public expectations are fueled by stakeholder arguments for research and public funding, coupled with intense media coverage in an ethically charged arena. We examine media representations in light of the expanding global landscape of SC clinical trials, asking what patients may realistically expect by way of timelines for the therapeutic and curative potential of regenerative medicine?MethodsWe built 2 international datasets: (1) 3,404 clinical trials (CT) containing 'stem cell*' from ClinicalTrials.gov and the World Health Organization's International Clinical Trials Registry Search Portal; and (2) 13,249 newspaper articles on SC therapies using Factiva.com. We compared word frequencies between the CT descriptions and full-text newspaper articles for the number containing terms for SC type and diseases/conditions. We also developed inclusion and exclusion criteria to identify novel SC CTs, mainly regenerative medicine applications.ResultsNewspaper articles focused on human embryonic SCs and neurological conditions with significant coverage as well of cardiovascular disease and diabetes. In contrast, CTs used primarily hematopoietic SCs, with an increase in CTs using mesenchymal SCs since 2007. The latter dominated our novel classification for CTs, most of which are in phases I and II. From the perspective of the public, expecting therapies for neurological conditions, there is limited activity in what may be considered novel applications of SC therapies.ConclusionsGiven the research, regulatory, and commercialization hurdles to the clinical translation of SC research, it seems likely that patients and political supporters will become disappointed and disillusioned. In this environment, proponents need to make a concerted effort to temper claims. Even though the field is highly promising, it lacks significant private investment and is largely reliant on public support, requiring a more honest acknowledgement of the expected therapeutic benefits and the timelines to achieving them.

Abstract While there have always been those in the American public who mistrust science and scientists' views of the world, they have tended to be a minority of the larger public. Recent COVID-19 related events indicate that could be changing for some key groups. What might explain the present state of mistrust of science within an important component of the American public? In this study, we delve deeply into this question and examine what citizens today believe about science and technology and why, focusing on core theories of trust, risk concern, and political values and on the important role of science optimism and pessimism orientations. Using national public survey data, we examine the correlates of science optimism and pessimism and test the efficacy of this construct as drivers of biotechnology policy. We find that science optimism and pessimism are empirically useful constructs and that they are important predictors of biotechnology policy choices.

ABSTRACT Experts’ views on the use of mostly digital technologies for dementia prevention are characterized by a simultaneity of “gerontechnological optimism” and skeptical hesitancy. Despite the hope for progress in dementia prevention through preventive technologies, experts also point to the complexity of prevention, the importance of environmental factors and public health policies, and the danger of an excessive focus on individual interventions. Without questioning the positive impact such technologies can have on many people, we claim that the experts’ ambiguity reveals a deeper concern, a kind of “cruel optimism” that is based on a fantasy of “supported autonomy”.

Multiple types of users (i.e. patients and care providers) have experiences with the same technologies in healthcare environments and may have different processes for developing trust in those technologies. The objective of this study was to assess how patients and care providers make decisions about the trustworthiness of mutually used medical technology in an obstetric work system. Using a grounded theory methodology, we conducted semi-structured interviews with 25 patients who had recently given birth and 12 obstetric healthcare providers to examine the decision-making process for developing trust in technologies used in an obstetric work system. We expected the two user groups to have similar criteria for developing trust in the technologies, though we found patients and physicians differed in processes for developing trust. Trust in care providers, the technologies' characteristics and how care providers used technology were all related to trust in medical technology for the patient participant group. Trustworthiness of the system and trust in self were related to trust in medical technology for the physician participant group. Our findings show that users with different perspectives of the system have different criteria for developing trust in medical technologies.

Bioethicists have long been concerned that seriously ill patients entering early phase (‘phase I’) treatment trials are motivated by therapeutic benefit even though the likelihood of benefit is low. In spite of these concerns, consent forms for phase I studies involving seriously ill patients generally employ indeterminate benefit statements rather than unambiguous statements of unlikely benefit. This seeming mismatch between attitudes and actions suggests a need to better understand research ethics committee members’ attitudes toward communication of potential benefits and risks of early phase studies to potential subjects. We surveyed the members of two U.S. research ethics committees using a phase I gene transfer study scenario, and compared the results to a previous survey of potential subjects’ perceptions and attitudes toward benefit and risk for the same protocol. The results show that there is indeed a gap between the subjects’ perceptions and the committee members’ views on what is appropriate to be communicated to research subjects. This discrepancy is the product of both the commonly assumed optimism of the subjects and to a “protective pessimism” of the research ethics committee members. We discuss this discrepancy using “frameworks of trust” and demonstrate the need to incorporate these frameworks into the existing model of informed consent.

Personalised medicine is widely considered as the way of the future for medicine. However, progress in cancer, with a few outstanding exceptions, has fallen below expectations because of the challenges of tumour heterogeneity and clonal evolution. In both benign and malignant disease, diseases caused by single genetic alterations are more amenable to precision medicine approaches. However, most common diseases are caused by a complex interplay of multiple genetic and environmental factors making personalised medicine far more challenging. The current optimism for personalised medicine is distorting clinical consultations, resource allocation and research funding prioritisation. A research active clinician must act both as an agent of change and development, and as a communicator of realism. Thus personalised medicine that includes a sober appreciation of what genomics can achieve, together with continued focus on the individual as a person not just as a genome, will contribute to further improvements in health and healthcare.

… precision medicine can produce at this time, while increasing the enthusiasm over what precision medicine … potential accomplishments of precision medicine are without bounds. Five …

… (AI) are prominent buzzwords when the topic of precision medicine comes up, but like many … separate the hype from the real-world impact. AI entered that breathless, over-hyped territory …

… precision medicine is likely to afford greatest hope and where instead our rhetoric may constitute hype… When we think about using data from precision medicine in AML we need to …

Precision medicine is defined as “an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person.”1 The goal is to define and identify meaningful subgroups and to apply tailored approaches for screening and/or treatments. While this initiative has gained traction since it was introduced in 2015 by the Precision Medicine Initiative, questions have been raised about how precision medicine compares to more “traditional” approaches, based on broadly targeting a disease or a few risk factors. Further, there are concerns about how precision medicine might be misleading or even fleecing the public.2 Precision-based therapeutic approaches for the treatment of colorectal cancer (CRC) appear to hold great promise and have already begun to transform clinical practice. For CRC prevention, precision strategies for identification of high-risk populations and tailored approaches have not yet fulfilled their hype and, furthermore, direct-to-consumer (DTC) genetic testing could be misleading regarding cancer risk. In this commentary, we provide perspective on the status of precision medicine for CRC treatment and screening by highlighting both the hope and hype of its clinical application.

… precision medicines, Schwartzberg added. “We need to become experts in multiple diseases and understand their biology if we want to use precision … prescribing precision medicines. …

Genomic and personalized medicine have become buzz phrases that pervade all fields of medicine. Rapid advances in "-omics" fields of research (chief of which are genomics, proteinomics, and epigenomics) over the last few years have allowed us to dissect the molecular signatures and functional pathways that underlie disease initiation and progression and to identify molecular profiles that help the classification of tumor subtypes and determine their natural course, prognosis, and responsiveness to therapies. Genomic medicine implements the use of traditional genetic information, as well as modern pangenomic information, with the aim of individualizing risk assessment, prevention, diagnosis, and treatment of cancers and other diseases. It is of note that personalizing medical treatment based on genetic information is not the revolution of the 21st century. Indeed, the use of genetic information, such as human leukocyte antigen-matching for solid organ transplantation or blood transfusion based on ABO blood group antigens, has been standard of care for several decades. However, in recent years rapid technical advances have allowed us to perform high-throughput, high-density molecular analyses to depict the genomic, proteinomic, and epigenomic make-up of an individual at a reasonable cost. Hence, the so-called genomic revolution is more or less the logical evolution from years of bench-based research and bench-to-bedside translational medicine.

… These developments have been associated with a range of terminologies, such as “precision medicine”, “personalized medicine”, “individualized medicine”, and “stratified medicine”. …

Representation shapes public attitudes and behaviors. With the recent advances and rapid adoption of LLMs, the way these systems are introduced will negotiate societal expectations for their role in high-stakes domains like health. Yet it remains unclear whether current narratives present a balanced view. We analyzed five prominent discourse channels (news, research press, YouTube, TikTok, and Reddit) over a two-year period on lexical style, informational content, and symbolic representation. Discussions were generally positive and episodic, with positivity increasing over time. Risk communication was unthorough and often reduced to information quality incidents, while explanations of LLMs’ generative nature were rare. Compared with professional outlets, TikTok and Reddit highlighted wellbeing applications and showed greater variations in tone and anthropomorphism but little attention to risks. We discuss implications for public discourse as a diagnostic tool in identifying literacy and governance gaps, and for communication and design strategies to support more informed LLM engagement.

While the technologies that enable Artificial Intelligence (AI) continue to advance rapidly, there are increasing promises regarding AI’s beneficial outputs and concerns about the challenges of human–computer interaction in healthcare. To address these concerns, institutions have increasingly resorted to publishing AI guidelines for healthcare, aiming to align AI with ethical practices. However, guidelines as a form of written language can be analyzed to recognize the reciprocal links between its textual communication and underlying societal ideas. From this perspective, we conducted a discourse analysis to understand how these guidelines construct, articulate, and frame ethics for AI in healthcare. We included eight guidelines and identified three prevalent and interwoven discourses: (1) AI is unavoidable and desirable; (2) AI needs to be guided with (some forms of) principles (3) trust in AI is instrumental and primary. These discourses signal an over-spillage of technical ideals to AI ethics, such as over-optimism and resulting hyper-criticism. This research provides insights into the underlying ideas present in AI guidelines and how guidelines influence the practice and alignment of AI with ethical, legal, and societal values expected to shape AI in healthcare.

One of the sectors for which Artificial Intelligence applications have been considered as exceptionally promising is the healthcare sector. As a public-facing sector, the introduction of AI applications has been subject to extended news coverage. This article conducts a quantitative and qualitative data analysis of English news media articles covering AI systems that allow the automation of tasks that so far needed to be done by a medical expert such as a doctor or a nurse thereby redistributing their agency. We investigated in this article one particular framing of AI systems and their agency: the framing that positions AI systems as (1a) replacing and (1b) outperforming the human medical expert, and in which (2) AI systems are personified and/or addressed as a person. The analysis of our data set consisting of 365 articles written between the years 1980 and 2019 will show that there is a tendency to present AI systems as outperforming human expertise. These findings are important given the central role of news coverage in explaining AI and given the fact that the popular frame of ‘outperforming’ might place AI systems above critique and concern including the Hippocratic oath. Our data also showed that the addressing of an AI system as a person is a trend that has been advanced only recently and is a new development in the public discourse about AI.

… Backed by a critical multimodal discourse analysis of the Dutch policy programme ‘Valuable AI’, we identify three tactics for imagining the future of AI and healthcare services, ie, tactics …

Background Artificial intelligence (AI) is increasingly proposed for use in health and health care systems. Beyond technical performance, public perceptions and affective responses influence whether AI technologies are accepted and adopted in real-world contexts. Social media platforms such as X (formerly Twitter) provide large-scale, real-time insight into public discourse surrounding emerging technologies, yet remain underused for examining how health AI is discussed, evaluated, and emotionally framed. Objective This study aimed to develop and apply large language model (LLM)–based methods for exploratory social listening on health AI. This is the first study to map large-scale sentiment, emotional expressions, and confidence-related signals in online discussions of applications of AI to health. Methods We collected 786,750 English-language posts from X (Twitter) published between January 1 and December 5, 2023, using health- and AI-related keywords. We benchmarked an LLM-based annotation framework by using OpenAI’s GPT-3.5-Turbo and GPT-4, comparing model classifications with trained human researchers. Annotations included overall sentiment and 6 evaluative domains frequently referenced in the literature surrounding attitudes toward health AI—usefulness, safety, privacy, ethics, quality, and trust. After cleaning, GPT-3.5-Turbo used the best-performing prompts to label 388,009 posts. A subset (n=268,347) was further analyzed using Emollama-7b, an open-source model fine-tuned from Meta’s LLaMA2-7B, for emotion detection, and latent Dirichlet allocation for thematic analysis. Comparisons were made across World Health Organization regions. Results Compared against human annotations, optimized prompts achieved weighted F1-scores above 0.60 across evaluative domains and sentiment classification. Global discourse about health AI was 65.26% (95% CI 65.11%-65.4%) positive and 83.62% (95% CI 83.48%-83.76%) emotionally optimistic, although substantial regional variation was observed in sentiment (P<.001). The Eastern Mediterranean and South-East Asia regions expressed significantly higher levels of positive sentiment and evaluative agreement in the studied features of health AI, alongside frequent discussion of the tech industry and commercial development. In comparison, the Western Pacific region expressed lower confidence and significantly more mentions of research topics (19.27%, 95% CI 18.5%-20.07%). Privacy was the most prominent global concern, with 33.31% (95% CI 32.98%-33.66%) of privacy-related posts expressing perceived risks. In the Region of the Americas, 18.19% (95% CI 17.92%-18.44%) of posts discussed algorithms and data governance, significantly higher than overall. Conclusions This study offers the first systematic characterization of online health AI discourse at scale, mapping stances toward key features of AI, emotional tone, and discussion topics across regions. LLM-powered social listening is demonstrated as a feasible approach for identifying dominant narratives and regionally distinct concerns, capable of surfacing opinions absent from traditional media. This can extend to studying discourse on other evolving health technologies where public surveying is limited. While methodological refinement and multilingual expansion are needed, this framework can inform timely policy development, risk communication, and responsible health AI governance.

Background Social media platforms facilitate global discourse on the application of artificial intelligence (AI) in healthcare. Nevertheless, there is a paucity of longitudinal analyses of digitally mediated discussions. Objective To investigate the evolution of global English-language-dominated discourse on #AIinHealthcare over a three-year period on X (formerly Twitter). Methods Using Fedica analytics, we analysed 57,880 tweets by 17,991 distinct users across 141 countries from 1 November 2022 to 1 November 2025. This analysis focused on English-language-dominant discourse around #AIinHealthcare (96.9% English), acknowledging hashtag-specific selection bias and linguistic limitations. This study used publicly available anonymised data and followed the ethical guidelines for social media research. Results The #AIinHealthcare garnered 39.2 million impressions, with significant contributions from high-income countries, notably the United States (40.7%) and Canada (21.0%), as well as India (13.4%; a rapidly expanding economy), collectively accounting for 75.1% of tweets and reflecting hashtag-specific, geographically concentrated engagement. This peaked in mid-2023 and stabilized lower by mid-2025. English was the predominant language of the discourse (96.9%). The community consisted of 74.9% grassroots users with fewer than 1,000 followers, suggesting genuine participation beyond elite influencers. Total engagement reached 72,625 interactions, primarily passive, comprising 68.1% likes, 19.4% retweets, 10.3% replies, and 2.1% quote tweets. Hashtag co-occurrence patterns, supported by qualitative inspection of exemplar tweets, indicated majorly five distinct clusters: foundational technical topics (#GenerativeAI, #ChatGPT, #LLMs) peaked after November 2022; clinical application themes emerged across disease-specific specialties (#Oncology, #Cardiology, #MentalHealth); healthcare implementation themes addressed practical integration (#DigitalHealth, #Telemedicine, #EHR); governance and ethics themes gained prominence (#ResponsibleAI, #AIEthics, #ExplainableAI, #DataPrivacy); and professional integration themes fostered learning communities (#MedTwitter, #MedicalEducation). Sentiment was predominantly neutral (95%), with positive (3%) and negative (2%). Monthly tweets peaked in mid-2023 at 1,600–1,800 before declining to 750–900 per month by June 2025. High-engagement content linked AI to practical applications, governmental initiatives, and clinical breakthroughs. Conclusion English-language-dominated discourse around #AIinHealthcare reveals hashtag-specific maturation from technical enthusiasm to governance and implementation focus. However, platform access restrictions in countries such as China and Russia may skew geographic representation. Disparities in sustainability discourse remain prevalent.

Objective Public perceptions of medical artificial intelligence (AI) directly influence its implementation and governance. While most existing research focuses on Western contexts, there is limited exploration of public responses in collectivist cultures and state-driven healthcare systems like China, particularly regarding the dynamic interplay of cognition, affect, and behavior. This study aims to fill this gap by examining public discourse on medical AI in China, with a specific focus on topic landscape, sentiment distribution, and the Cognition-Affect-Behavior (CAB) mechanisms driving governance. Methods We collected 12,356 valid Weibo posts on medical AI from January 2022 to December 2024. The Latent Dirichlet Allocation (LDA) topic modeling identified key topics, sentiment analysis assessed emotional tendencies, and grounded theory analysis was applied to 1,000 posts using open, axial, and selective coding to construct a theoretical model. Results The findings revealed that public discussions covered eight key topics, categorized into three dimensions: foundational drivers of medical AI development, application domains of medical AI, and societal benefits and risks challenges. All topics exhibited a coexistence of positive and negative emotions. The CAB model showed that, cognitively, the public emphasized the human core of healthcare, while acknowledging AI’s efficacy, leading to a collaborative augmentation model for the physician-AI integration, where decision-making is physician-led, and AI serves as a supportive tool. Emotionally, the public expressed both amazement at AI’s capabilities and expectations for physician-AI integration, alongside resistance to AI and anxiety about the physician-AI integration. Behaviorally, three proactive agency governance strategies were observed, which either reinforced or recalibrated existing cognitive frameworks. Conclusion This study provides valuable insights into the public’s cognitive and emotional responses, as well as proactive behaviors toward medical AI in China. It also highlights the emergence of bottom-up accountability mechanisms, where civic engagement shapes the development of AI governance frameworks in healthcare.

Abstract Background The rapid emergence of artificial intelligence–based large language models (LLMs) in 2022 has initiated extensive discussions within the academic community. While proponents highlight LLMs’ potential to improve writing and analytical tasks, critics caution against the ethical and cultural implications of widespread reliance on these models. Existing literature has explored various aspects of LLMs, including their integration, performance, and utility, yet there is a gap in understanding the nature of these discussions and how public perception contrasts with expert opinion in the field of public health. Objective This study sought to explore how the general public’s views and sentiments regarding LLMs, using OpenAI’s ChatGPT as an example, differ from those of academic researchers and experts in the field, with the goal of gaining a more comprehensive understanding of the future role of LLMs in health care. Methods We used a hybrid sentiment analysis approach, integrating the Syuzhet package in R (R Core Team) with GPT-3.5, achieving an 84% accuracy rate in sentiment classification. Also, structural topic modeling was applied to identify and analyze 8 key discussion topics, capturing both optimistic and critical perspectives on LLMs. Results Findings revealed a predominantly positive sentiment toward LLM integration in health care, particularly in areas such as patient care and clinical decision-making. However, concerns were raised regarding their suitability for mental health support and patient communication, highlighting potential limitations and ethical challenges. Conclusions This study underscores the transformative potential of LLMs in public health while emphasizing the need to address ethical and practical concerns. By comparing public discourse with academic perspectives, our findings contribute to the ongoing scholarly debate on the opportunities and risks associated with LLM adoption in health care.

Introduction Voice as a biomarker has emerged as a transformative field in health technology, providing non-invasive, accessible, and cost-effective methods for detecting, diagnosing, and monitoring various conditions. Start-ups are at the forefront of this innovative field, developing and marketing clinical voice AI solutions to a range of healthcare actors and shaping the field's early development. However, there is limited understanding of how start-ups in this field frame their innovations, and address—or overlook—critical socio-ethical, technical, and regulatory challenges in the rapidly evolving field of digital health. Methods This study uses discourse analysis to examine the language on the public websites of 25 voice AI health-tech start-ups. Grounded in constitutive discourse analysis, which asserts that discourse both reflects and shapes realities, the study identifies patterns in how these companies describe their identities, technologies, and datasets. Results The analysis shows start-ups consistently highlight the efficacy, reliability, and safety of their technologies, positioning them as transformative healthcare solutions. However, descriptions of voice datasets used to train algorithms vary widely and are often absent, reflecting broader gaps in acoustic and ethical standards for voice data collection and insufficient incentives for start-ups to disclose key data details. Discussion Start-ups play a crucial role in the research, development, and marketization of voice AI health-tech, prefacing the integration of this new technology into healthcare systems. By publicizing discourse around voice AI technologies at this early stage, start-ups are shaping public perceptions, setting expectations for end-users, and ultimately influencing the implementation of voice AI technologies in healthcare. Their discourse seems to strategically present voice AI health-tech as legitimate by using promissory language typical in the digital health field and showcase the distinctiveness from competitors. This analysis highlights how this double impetus often drives narratives that prioritize innovation over transparency. We conclude that the lack of incentive to share key information about datasets is due to contextual factors that start-ups cannot control, mainly the absence of clear standards and regulatory guidelines for voice data collection. Addressing these complexities is essential to building trust and ensuring responsible integration of voice AI into healthcare systems.

… Our findings reveal four dominant themes: (1) healthcare AI … and ethical AI; and (4) the socioeconomic ramifications of AI … While the Commission frequently emphasizes “trustworthy AI…

Artificial intelligence (AI) has captured the interest of multiple actors with speculations about its benefits and dangers. Despite increasing scholarly attention to the discourses of AI, there are limited insights on how different groups interpret and debate AI and shape its opportunities for action. We consider AI an issue field understood as a contested phenomenon where heterogeneous actors assert and debate the meanings and consequences of AI. Drawing on computational social science methods, we analyzed large amounts of text on how politicians (parliamentarians) consultancies (high reputation firms), and lay experts (AI-forum Reddit users) articulate meanings about AI. Through topic modeling, we identified diverse and co-existing discourses: politicians predominantly articulated AI as a societal issue requiring an ethical response, consultancies stressed AI as a business opportunity pushing a transformation-oriented discourse, and lay experts expressed AI as a technical issue shaping a techno-feature discourse. Moreover, our analysis details the hopes and fears within AI discourses, revealing that sentiment varies by actor group. Based on these findings, we contribute new insights about AI as an issue field shaped by the discursive work performed by heterogeneous actors.

… The results of this study reveal a complex and multifaceted public perception of artificial intelligence in the context of healthcare, particularly in medical diagnosis. Several key themes …

Algorithmic technologies, concepts, and practices as sociotechnical constructs emerge and proliferate through language in society. Discourses around artificial intelligence (AI) shape our collective imagination, affect technological development, and influence policymaking. What can we learn from critically examining wide-ranging discourses around AI, using a mix of qualitative methods and natural language processing (NLP), both among actors who influence its development and the publics who are affected by it? In this chapter, we examine the “language of algorithms” to make sense of AI Watch reports and stakeholder responses to the proposed AI Act in the European Union. Linguistic devices such as metaphors, metonymy, and personification reveal how we conceptualize, narrate, contest, or attribute agency to AI systems. Saying that AI is “trustworthy”, “biased”, or “transforming society” are discursive acts that implicitly attribute a sense of agency to technology rather than the human actors involved in its creation. Critically examining such AI discourses reveals how language affects attitudes, influences practices and policies, and shapes future imaginaries around AI.

… discourse discourse generated by academics has recently adopted a more technical and cautious tone, and AI AI … the empowerment empowerment of AI systems before their generative …