Utility of ChatGPT in Clinical Practice
Open accessPublished version of a preprint
Abstract
ChatGPT is receiving increasing attention and has a variety of application scenarios in clinical practice. In clinical decision support, ChatGPT has been used to generate accurate differential diagnosis lists, support clinical decision-making, optimize clinical decision support, and provide insights for cancer screening decisions. In addition, ChatGPT has been used for intelligent question-answering to provide reliable information about diseases and medical queries. In terms of medical documentation, ChatGPT has proven effective in generating patient clinical letters, radiology reports, medical notes, and discharge summaries, improving efficiency and accuracy for health care providers. Future research directions include real-time monitoring and predictive analytics, precision medicine and personalized treatment, the role of ChatGPT in telemedicine and remote health care, and integration with existing health care systems. Overall, ChatGPT is a valuable tool that complements the expertise of health care providers and improves clinical decision-making and patient care. However, ChatGPT is a double-edged sword. We need to carefully consider and study the benefits and potential dangers of ChatGPT. In this viewpoint, we discuss recent advances in ChatGPT research in clinical practice and suggest possible risks and challenges of using ChatGPT in clinical practice. It will help guide and support future artificial intelligence research similar to ChatGPT in health.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The role of AI in augmenting physician clinical decision-making in the management of chronic conditions for individual patients in primary care clinics is still largely unknown.
“The role of AI in augmenting physician CDM in the management of chronic conditions for individual patients in the PCC is still largely unknown. 5”
From The use of an artificial intelligence platform OpenEvidence to augment clinical decision-making for primary care physicians (2025) · cites this paper as [5]
The Introduction says studies evaluating ChatGPT in clinical practice are lacking, and Possible Risks and Challenges says its recommendations need clinical assessment. That supports uncertainty about its role in physician decision-making, but the paper does not establish the claim specifically for chronic-condition management of individual patients in primary care clinics. Its discussion also concerns ChatGPT rather than AI generally. (LLM, conf=0.84)
Experimental — accuracy not yet measuredChatGPT offers numerous benefits in health care, including optimizing radiology reporting, generating patient discharge summaries, improving patient care, providing antimicrobial advice, and improving clinical decision support.
“In health care, ChatGPT offers numerous benefits [17], including optimizing radiology reporting [18], generating patient discharge summaries [19], improving patient care [20], providing antimicrobial advice [21], and improving clinical decision support [22].”
From Ethical Considerations of Using ChatGPT in Health Care (2023) · cites this paper as [17]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Kaugeranna, R., Kaugeranna, E., & Aion, (. S. 4. 6. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.giscience.2023.43 · Cited by 13,695 · see citing works
Cascella, M., Montomoli, J., Bellini, V., & Bignami, E. (2023) · Journal of Medical Systems · doi:10.1007/s10916-023-01925-4 · Cited by 1,120 · see citing works
ChatGPT and other large language models are double-edged swords
Shen, Y., Heacock, L., Elias, J., Hentel, K. D., Reig, B., Shih, G., & Moy, L. (2023) · Radiology · doi:10.1148/radiol.230163 · Cited by 1,008 · see citing works
ChatGPT: the future of discharge summaries?
Patel, S. B., & Lam, K. (2023) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00021-3 · Cited by 728 · see citing works
Yeo, Y. H., Samaan, J. S., Ng, W. H., Ting, P., Trivedi, H. D., Vipani, A., Ayoub, W. S., Yang, J. D., Liran, O., Spiegel, B. M. R., et al. (2023) · Clinical and Molecular Hepatology · doi:10.3350/cmh.2023.0089 · Cited by 655 · see citing works
The Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review
Milne-Ives, M., de Cock, C., Lim, E., Shehadeh, M. H., de Pennington, N., Mole, G., Normando, E., & Meinert, E. (2020) · Journal of Medical Internet Research · doi:10.2196/20346 · Cited by 626 · see citing works
ChatGPT makes medicine easy to swallow: an exploratory case study on simplified radiology reports
Jeblick, K., Schachtner, B., Dexl, J., Mittermeier, A., Stüber, A. T., Topalis, J., Weber, T., Wesp, P., Sabel, B. O., Ricke, J., et al. (2023) · European Radiology · doi:10.1007/s00330-023-10213-1 · Cited by 549 · see citing works
Rao, A. S., Kim, J., Kamineni, M., Pang, M., Lie, W., Dreyer, K. J., & Succi, M. D. (2023) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2023.05.003 · Cited by 481 · see citing works
Rao, A., Pang, M., Kim, J., Kamineni, M., Lie, W., Prasad, A. K., Landman, A., Dreyer, K., & Succi, M. D. (2023) · Journal of Medical Internet Research · doi:10.2196/48659 · Cited by 412 · see citing works
Using ChatGPT to write patient clinic letters
Ali, S. R., Dobbs, T. D., Hutchings, H. A., & Whitaker, I. S. (2023) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00048-1 · Cited by 375 · see citing works
Hirosawa, T., Harada, Y., Yokose, M., Sakamoto, T., Kawamura, R., & Shimizu, T. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20043378 · Cited by 344 · see citing works
ChatGPT: Is this version good for healthcare and research?
Vaishya, R., Misra, A., & Vaish, A. (2023) · Diabetes & Metabolic Syndrome: Clinical Research & Reviews · doi:10.1016/j.dsx.2023.102744 · Cited by 293 · see citing works
Johnson, S. B., King, A. J., Warner, E. L., Aneja, S., Kann, B. H., & Bylund, C. L. (2023) · JNCI Cancer Spectrum · doi:10.1093/jncics/pkad015 · Cited by 289 · see citing works
Evaluating ChatGPT as an Adjunct for Radiologic Decision-Making
Rao, A., Kim, J., Kamineni, M., Pang, M., Lie, W., & Succi, M. D. (2023) · doi:10.1101/2023.02.02.23285399 · Cited by 257 · see citing works
The exciting potential for ChatGPT in obstetrics and gynecology
Grünebaum, A., Chervenak, J., Pollet, S. L., Katz, A., & Chervenak, F. A. (2023) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2023.03.009 · Cited by 234 · see citing works
Artificial intelligence‐based chatbot patient information on common retinal diseases using ChatGPT
Potapenko, I. O., Boberg‐Ans, L. C., Hansen, M. S., Klefter, O. N., van Dijk, E. H. C., & Subhi, Y. (2023) · Acta Ophthalmologica · doi:10.1111/aos.15661 · Cited by 148 · see citing works
Assessing the utility of ChatGPT throughout the entire clinical workflow
Rao, A., Pang, M., Kim, J., Kamineni, M., Lie, W., Prasad, A. K., Landman, A., Dreyer, K. J., & Succi, M. D. (2023) · doi:10.1101/2023.02.21.23285886 · Cited by 134 · see citing works
Medically Aware GPT-3 as a Data Generator for Medical Dialogue Summarization
Chintagunta, B., Katariya, N., Amatriain, X., & Kannan, A. (2021) · doi:10.18653/v1/2021.nlpmc-1.9 · Cited by 122 · see citing works
ChatGPT Makes Medicine Easy to Swallow: An Exploratory Case Study on Simplified Radiology Reports
Jeblick, K., Schachtner, B., Dexl, J., Mittermeier, A., Stüber, A. T., Topalis, J., Weber, T., Wesp, P., Sabel, B., Ricke, J., et al. (2022) · arXiv · doi:10.48550/arxiv.2212.14882 · Cited by 97 · see citing works
Assessing the Value of ChatGPT for Clinical Decision Support Optimization
Liu, S., Wright, A. P., Patterson, B. L., Wanderer, J. P., Turer, R. W., Nelson, S. D., McCoy, A. B., Sittig, D. F., & Wright, A. (2023) · doi:10.1101/2023.02.21.23286254 · Cited by 90 · see citing works
Self-citationChatGPT in Surgical Practice—a New Kid on the Block
Bhattacharya, K., Bhattacharya, A. S., Bhattacharya, N., Yagnik, V. D., Garg, P. K., & Kumar, S. (2023) · Indian Journal of Surgery · doi:10.1007/s12262-023-03727-x · Cited by 75 · see citing works
Common errors in writing the cause of death certificate in the Middle East
Madadin, M. S., Alhumam, A. S., Bushulaybi, N. A., et al. (2019) · Journal of Forensic and Legal Medicine · doi:10.1016/j.jflm.2019.101864 · Cited by 25 · see citing works
Rababa’h, A. M., Mardini, A. N., Ababneh, M., et al. (2022) · International Journal of Critical Illness and Injury Science · doi:10.4103/ijciis.ijciis_72_21 · Cited by 13 · see citing works
Editorial: Machine Learning in Clinical Decision-Making
Filiberto, A. C., Leeds, I. L., & Loftus, T. J. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.784495 · Cited by 12 · see citing works
Flagged first, then by citations
Research works printed but not linked: 4
- [2]Brown TB Mann B Ryder N Subbiah M Kaplan J Dhariwal P Neelakantan A Shyam P Sastry G Askell A Agarwal S Herbert-Voss A Krueger G Henighan T Child R Ramesh A Ziegler DM Wu J Winter C Hesse C Chen M Sigler E Litwin M Gray S Chess B Clark J Berner C McCandlish S Radford A Sutskever I Amodei D Language models are few-shot learners arXiv Preprint posted online on May 28, 2020 10.48550/arXiv.2005.14165 · DOI · Paper page
- [4]Sakib MSI What is ChatGPT? ResearchGate 2023 02 2023-06-20 https://www.researchgate.net/publication/367794587_What_is_ChatGPT
- [17]Jialin L Siru L Essential Clinical Informatics 2019 04 Chengdu, China Sichuan Science and Technology Press
- [22]Joshi A Katariya N Amatriain X Kannan A Dr. Summarize: global summarization of medical dialogue by exploiting local structures arXiv Preprint posted online on September 18, 2020 10.48550/arXiv.2009.08666 · DOI
Cited by
Sorted by citations
Ethical Considerations of Using ChatGPT in Health Care
Wang, C., Liu, S., Yang, H., Guo, J., Wu, Y., & Liu, J. (2023) · Journal of Medical Internet Research · doi:10.2196/48009 · Cited by 541 · see citing works
Self-citationYounis, H. A., Eisa, T. A. E., Nasser, M., Sahib, T. M., Noor, A. A., Alyasiri, O. M., Salisu, S., Hayder, I. M., & Younis, H. A. (2024) · Diagnostics · doi:10.3390/diagnostics14010109 · Cited by 242 · see citing works
Assessing the research landscape and clinical utility of large language models: a scoping review
Park, Y.-J., Pillai, A., Deng, J., Guo, E., Gupta, M., Paget, M., & Naugler, C. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02459-6 · Cited by 187 · see citing works
Alkhaaldi, S. M. I., Kassab, C. H., Dimassi, Z., Oyoun Alsoud, L., Al Fahim, M., Al Hageh, C., & Ibrahim, H. (2023) · JMIR Medical Education · doi:10.2196/51302 · Cited by 132 · see citing works
Yamamoto, A., Koda, M., Ogawa, H., Miyoshi, T., Maeda, Y., Otsuka, F., & Ino, H. (2024) · JMIR Medical Education · doi:10.2196/58753 · Cited by 92 · see citing works
“In the medical domain, their integration is being considered in both clinical and research contexts [9].”
BackgroundWu, Y., Wu, M., Wang, C., Lin, J., Liu, J., & Liu, S. (2024) · JMIR Medical Informatics · doi:10.2196/54811 · Cited by 87 · see citing works
Self-citationHurt, R. T., Stephenson, C. R., Gilman, E. A., Aakre, C. A., Croghan, I. T., Mundi, M. S., Ghosh, K., & Edakkanambeth Varayil, J. (2025) · Journal of Primary Care & Community Health · doi:10.1177/21501319251332215 · Cited by 78 · see citing works
Shao, C.-Y., Li, H., Liu, X.-L., Li, C., Yang, L.-Q., Zhang, Y.-J., Luo, J., & Zhao, J. (2023) · Interactive Journal of Medical Research · doi:10.2196/46900 · Cited by 49 · see citing works
Leveraging Large Language Models for Generating Responses to Patient Messages
Liu, S., McCoy, A. B., Wright, A. P., Carew, B., Genkins, J. Z., Huang, S. S., Peterson, J. F., Steitz, B., & Wright, A. (2023) · doi:10.1101/2023.07.14.23292669 · Cited by 43 · see citing works
Self-citationCherrez-Ojeda, I., Gallardo-Bastidas, J. C., Robles-Velasco, K., Osorio, M. F., Velez Leon, E. M., Leon Velastegui, M., Pauletto, P., Aguilar-Díaz, F. C., Squassi, A., González Eras, S. P., et al. (2024) · JMIR Medical Education · doi:10.2196/51757 · Cited by 46 · see citing works
A bibliometric analysis of the advance of artificial intelligence in medicine
Lin, M., Lin, L., Lin, L., Lin, Z., & Yan, X. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1504428 · Cited by 36 · see citing works
Generative AI and the future of connectivist learning in higher education
Liang, E. S., & Bai, S. (2025) · Journal of Asian Public Policy · doi:10.1080/17516234.2024.2386085 · Cited by 34 · see citing works
ChatGPT in Answering Queries Related to Lifestyle-Related Diseases and Disorders
Mondal, H., Dash, I., Mondal, S., & Behera, J. K. (2023) · Cureus · doi:10.7759/cureus.48296 · Cited by 32 · see citing works
Yong, L. P. X., Tung, J. Y. M., Lee, Z. Y., Kuan, W. S., & Chua, M. T. (2024) · Journal of Medical Internet Research · doi:10.2196/56413 · Cited by 12 · see citing works
Lv, J., Xu, Y., Jiang, M., Lv, Y., Sun, J., Lu, J., Wang, L., & Wang, H. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf109 · Cited by 12 · see citing works
Deng, J., Li, L., Oosterhof, J. J., Malliaras, P., Silbernagel, K. G., Breda, S. J., Eygendaal, D., Oei, E. H., & de Vos, R.-J. (2025) · Musculoskeletal Science and Practice · doi:10.1016/j.msksp.2025.103275 · Cited by 12 · see citing works
Clinical AI Scribes in primary care: accuracy, error severity and implications for clinical practice
Draper, T. C., Cox, T., Lamb-Riddell, K., Moretti, L. A., McCormick, J., Trowell, S., Kiely, J., & Luxton, R. (2025) · BMJ Digital Health & AI · doi:10.1136/bmjdhai-2025-000092 · Cited by 10 · see citing works
Agentic LLM Workflows for Generating Patient-Friendly Medical Reports
Sudarshan, M., Shih, S., Yee, E., Yang, A., Zou, J., Chen, C., Zhou, Q., Chen, L., Singhal, C., & Shih, G. (2024) · arXiv · doi:10.48550/arxiv.2408.01112 · Cited by 8 · see citing works
“In recent years, Large Language Models (LLMs) have been increasingly leveraged in healthcare applications, from producing discharge summaries [5] to structured radiology reports [6].”
BackgroundAI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator
Fan, Z., Tang, J., Chen, W., Wang, S., Wei, Z., Xi, J., Huang, F., & Zhou, J. (2024) · arXiv · doi:10.48550/arxiv.2402.09742 · Cited by 6 · see citing works
- Authors
- Liu, J., Wang, C., & Liu, S.
- Affiliations
- Sichuan University (CN)
- Vanderbilt University Medical Center (US)
- Corresponding author
- Liu, Siru
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2023-06-28
- Volume
- 25
- Pages
- e48568
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC10365580
- OpenAlex
- W4380995257
- Cited by
- 505
- References
- 24
- Citations by year
- 2026: 68 · 2025: 186 · 2024: 200 · 2023: 49
- FWCI
- 20.03
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- ChatGPT, large language models, clinical decision support, differential diagnosis, cancer screening, medical question answering, radiology reports, discharge summaries, telemedicine, predictive analytics, precision medicine
- MeSH terms
- Clinical Decision-Making, Artificial Intelligence, Diagnosis, Differential, Documentation, Humans, Organizations
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
