AI literature review writer
It gathers 40+ real papers first, then drafts an outline and writes chapter by chapter. Every citation links back to the original paper.
Attach up to 10 PDF or Word papers you have already read. Credits are charged by actual usage. See credit details
How to write a literature review with AI
- 1
Enter your topic
Write one line on the topic and scope, and attach papers you have read. Chinese and English literature are searched together.
- 2
Confirm the reading list
It searches from 4–8 angles in rounds, screens and deduplicates, and lists 40–60 papers grouped by direction, each noting where it fits in the review.
- 3
Confirm the outline
Papers are grouped into 3–7 main chapters, and each section names its sources and the claim it argues.
- 4
Write chapter by chapter
Each chapter is written against its sources, then the abstract is added and references are generated from the citations actually used.
Fast to write, solid enough for your supervisor
Each stage produces a file you can open and edit directly.
Every citation is a real paper
The text only cites papers from the reading list, found by search or uploaded by you, and each citation links to the DOI or original page. References are numbered from the citations actually used, with authors and venues taken from search results.
Organized by argument, not paper by paper
Each paragraph opens with a claim. Papers that agree support it in one sentence, and conflicting findings are set side by side with the difference explained. It never gives one paper per paragraph.
You confirm every step
The reading list, outline and text are produced in turn, and it stops after each one for you to confirm. Delete a line in the list to exclude that paper; change a section's claim in the outline and that section is written to the new one.
Or use only your own papers
Attached PDFs and Word files all go into the reading list, and search only fills angles they do not cover. The full settings also import from Zotero or EndNote, or skip search and use uploaded papers only.
Watch a literature review being written
From topic to finished text, it stops at every stage for you to confirm. The example is written to show the process; all cited papers are real.
2. Medical knowledge and exam performance
Large language models now perform near the passing level on standardized medical exams: ChatGPT scored around the threshold on all three steps of the USMLE , and when broken down by question source and difficulty its performance matched that of a third-year medical student . Models tuned for the medical domain have narrowed the gap with clinicians further . Yet all of this evidence comes from multiple-choice testing; whether exam scores translate into explanation and feedback in teaching still needs classroom studies to answer.
References
- Kung T H, Cheatham M, Medenilla A, et al. Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLOS Digital Health, 2023.
- Gilson A, Safranek C W, Huang T, et al. How Does ChatGPT Perform on the United States Medical Licensing Examination (USMLE)? JMIR Medical Education, 2023.
- Singhal K, Azizi S, Tu T, et al. Large language models encode clinical knowledge. Nature, 2023.
Large language models in medical education, focusing on assessment and teaching
I collected 46 papers across 5 directions: exam assessment, clinical knowledge, teaching use, evaluation methods and ethical risks. Edit the list directly or tell me what to add or remove.
The outline has 6 chapters and 14 sections, each with the claim it argues. Any changes to the structure?
The text is done: about 6,000 words citing 38 papers, open on the left.
Kung et al., 2023Kung T H, Cheatham M, Medenilla A, et al. Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLOS Digital Health, 2023.Open originalEverything a review needs, done properly
Multi-angle search in Chinese and English
The topic is split into 4–8 angles, with a set of Chinese and English search terms for each, searched in rounds. If an angle turns up too little, it rephrases and searches again.
Every outline section states a claim
Each section's claim says what the reader should believe after reading it, not "introduce studies on X". The writing organizes evidence around that claim.
Beyond reviews: argue a claim
With "Argue a claim", it first suggests 3 arguable claims from different angles based on the reading list; once you pick one, the whole paper organizes supporting, qualifying and opposing evidence around it. It can also follow a chapter structure you give.
Acknowledged in published SCI papers
Published papers state in their acknowledgments or AI-use statements that Acadwrite was used to polish the language.
- Theranostics2024Microfluidic chips in female reproduction: a systematic review of status, advances, and challenges
During the preparation of this work the authors used Acadwrite in order to improve readability, grammar and language style.
- Frontiers in Neuroscience2024Physical exercise for brain plasticity promotion an overview of the underlying oscillatory mechanism
We would like to thank Acadwrite (acadwrite.cn) for English language editing.
- Poultry Science2025Effects of induced molting on lipid accumulation in liver of aged laying hens
During the preparation of this work authors used (https://acadwrite.cn/) in order to improve language.
- Aging Medicine2025The Mechanism and Potential Therapeutic Strategies of Vascular Aging
Acadwrite AI (https://acadwrite.cn/apps/polish) was utilized for polishing the English language of the manuscript to enhance readability.
- Interactive Journal of Medical Research2025Evolution of Artificial Intelligence in Medical Education From 2000 to 2024: Bibliometric Analysis
During the preparation of this work, the authors used Acadwrite to improve readability, grammar, and language style.
AI literature review FAQ
Didn't find your answer? Reviews are charged by actual usage; see credit details. See credit details
Are the citations in an AI literature review real?
The text only cites papers in the reading list, found by search or uploaded by you. Every citation links to the original, and references are generated from the citations actually used, so nothing is made up. We still recommend spot-checking key citations before you submit.
How many papers does a review cite?
The reading list usually holds 40–60 papers and the text cites at least 30. If the topic is too narrow and still has fewer than 40 after extra searches, the report says so.
Which sources are searched?
Journal articles, arXiv preprints and PubMed, in Chinese and English, with an optional publication year range.
Can I use papers I have already read?
Yes. Attach up to 10 PDF or Word papers and they all go into the reading list. You can also bulk import from Zotero or EndNote, or use only those papers without searching.
Can it write a review in English?
Yes. Set the review language to English and the search, outline and text are all done in English, with references generated the same way.
Can I submit the review as is?
It is a complete first draft with real citations. You remain responsible for which arguments to keep and the final reasoning, and you can keep rewriting chapters or polishing in the same workspace.
How do you write a literature review?
A review usually has an introduction, main chapters organized by research theme, and a discussion and outlook. The main body is organized by research question, with each paragraph opening on a claim backed by papers, rather than summarizing one paper after another.
How is this different from asking ChatGPT or DeepSeek?
General chat tends to return the whole review at once, and its citations may not exist. Here it searches real papers first, you confirm the list and outline, and only then is it written chapter by chapter, with every stage a file you can edit.
How much does it cost?
Sign-in is required. The review runs in the workspace and is charged by tokens used, at 20 credits per million tokens; a full review takes about 8–9 credits. See the credits page for details.