ChatGPT's Ability to Assist with Clinical Documentation: A Randomized Controlled Trial
Abstract
INTRODUCTION: Clinical documentation is a critical aspect of health care that enables healthcare providers to communicate effectively with each other and maintain accurate patient care records. Artificial intelligence tools, such as chatbots and virtual assistants, have the potential to assist healthcare providers in clinical documentation. ChatGPT is an artificial intelligence conversational model that generates human-like responses to text-based prompts. In this study, we sought to investigate ChatGPT's ability to assist with writing a history of present illness based on standardized patient histories. METHODS: A blinded, randomized controlled study was conducted to compare the use of typing, dictation, and ChatGPT as tools to document history of present illness (HPI) of standardized patient histories. Eleven study participants, consisting of medical students, orthopaedic surgery residents, and attending surgeons, completed three HPIs using a different documentation technique for each one. Participants were randomized into cohorts based on the type of documentation technique. Participants were asked to interview standardized patients and document the patient's history of present illness using their assigned method. RESULTS: ChatGPT was found to be intermediate for speed; dictation was fastest, but produced markedly longer and higher quality patient histories based on Physician Documentation Quality Instrument score compared with dictation and typing. However, ChatGPT included erroneous information in 36% of the documents. Poor agreement existed on the quality of patient histories between reviewers. DISCUSSION: Our study suggests that ChatGPT has the potential to improve clinical documentation by producing more comprehensive and organized HPIs. ChatGPT can generate longer and more detailed documentation compared with typing or dictation documentation methods. However, additional studies are needed to investigate and address concerns regarding privacy, bias, and accuracy of information.
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When compared to previous literature, AI-driven documentation varied between 'bad (29.7)' and 'excellent (35.6)'.
“When compared to previous literature AI-driven documentation varied between “bad (29.7)” and “excellent (35.6)” highlighting the variability in documentation produced by AI systems [23, 25, 28].”
From Artificial Intelligence (AI) – Powered Documentation Systems in Healthcare: A Systematic Review (2025) · cites this paper as [23]
The combination of dictation and Chat GPT would create the greatest efficiency gains.
“authors concluded that the combination of dictation and chat GPT would create the greatest efficiency gains [23].”
From Artificial Intelligence (AI) – Powered Documentation Systems in Healthcare: A Systematic Review (2025) · cites this paper as [23]
Dictation was identified as a non-significant faster alternative to Chat GPT.
“Baker et al. identified dictation as a non-significant faster alternative to Chat GPT”
From Artificial Intelligence (AI) – Powered Documentation Systems in Healthcare: A Systematic Review (2025) · cites this paper as [23]
Cites
Large language models encode clinical knowledge
Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al. (2023) · Nature · doi:10.1038/s41586-023-06291-2 · Cited by 4,083 · see citing works
Ayers, J. W., Poliak, A., Dredze, M., Leas, E. C., Zhu, Z., Kelley, J. B., Faix, D. J., Goodman, A. M., Longhurst, C. A., Hogarth, M., et al. (2023) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2023.1838 · Cited by 2,613 · 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
Korngiebel, D. M., & Mooney, S. D. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00464-x · Cited by 259 · see citing works
Assessing electronic note quality using the physician documentation quality instrument (PDQI-9)
Bakken, S., Wrenn, J. O., Siegler, E. L., & Stetson, P. D. (2012) · Applied Clinical Informatics · doi:10.4338/aci-2011-11-ra-0070 · Cited by 182 · see citing works
Weiner, S. J., Wang, S., Kelly, B., Sharma, G., & Schwartz, A. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa027 · Cited by 100 · see citing works
Wang, A., Dunlop, W., Rodda, H., Ben-Meir, M., Staples, M., & Walker, K. J. (2017) · Applied Clinical Informatics · doi:10.4338/aci2017050080 · Cited by 35 · see citing works
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Miao, J., Thongprayoon, C., Suppadungsuk, S., Valencia, O. A. G., & Cheungpasitporn, W. (2024) · Medicina · doi:10.3390/medicina60030445 · Cited by 176 · see citing works
Generative artificial intelligence in primary care: an online survey of UK general practitioners
Blease, C., Locher, C., Gaab, J., Hägglund, M., & Mandl, K. D. (2024) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2024-101102 · Cited by 169 · see citing works
Artificial Intelligence (AI) – Powered Documentation Systems in Healthcare: A Systematic Review
Bracken, A., Reilly, C., Feeley, A., Sheehan, E., Merghani, K., & Feeley, I. (2025) · Journal of Medical Systems · doi:10.1007/s10916-025-02157-4 · Cited by 113 · see citing works
“…has been reported with the use of the physician documentation quality instrument (PDQI-9 tool) [34] When compared to previous literature AI-driven documentation varied between “bad (29.7)” and “excellent (35.6)” highlighting the variability in documentation produced by AI systems [23, 25, 28].”
InfluentialBlease, C., Worthen, A., & Torous, J. (2024) · Psychiatry Research · doi:10.1016/j.psychres.2024.115724 · Cited by 113 · see citing works
ChatGPT-4 Omni Performance in USMLE Disciplines and Clinical Skills: Comparative Analysis
Bicknell, B. T., Butler, D., Whalen, S., Ricks, J., Dixon, C. J., Clark, A. B., Spaedy, O., Skelton, A., Edupuganti, N., Dzubinski, L., et al. (2024) · JMIR Medical Education · doi:10.2196/63430 · Cited by 107 · see citing works
Examining the Role of Large Language Models in Orthopedics: Systematic Review
Zhang, C., Liu, S., Zhou, X., Zhou, S., Tian, Y., Wang, S., Xu, N., & Li, W. (2024) · Journal of Medical Internet Research · doi:10.2196/59607 · Cited by 66 · see citing works
Toward Clinical Generative AI: Conceptual Framework
Bragazzi, N. L., & Garbarino, S. (2024) · JMIR AI · doi:10.2196/55957 · Cited by 64 · see citing works
Galloway, J. L., Munroe, D., Vohra-Khullar, P. D., Holland, C., Solis, M. A., Moore, M. A., & Dbouk, R. H. (2024) · Journal of General Internal Medicine · doi:10.1007/s11606-024-08924-2 · Cited by 56 · see citing works
Schwieger, A., Angst, K., de Bardeci, M., Burrer, A., Cathomas, F., Ferrea, S., Grätz, F., Knorr, M. S., Kronenberg, G., Spiller, T. R., et al. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105654 · Cited by 45 · see citing works
Omar, M., Nadkarni, G. N., Klang, E., & Glicksberg, B. S. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000662 · Cited by 49 · see citing works
Kunze, K. N., Varady, N. H., Mazzucco, M. R., Lu, A. Z., Chahla, J. A., Martin, R. K., Ranawat, A. S., Pearle, A. D., & Williams, R. J. (2024) · Arthroscopy The Journal of Arthroscopic and Related Surgery · doi:10.1016/j.arthro.2024.06.021 · Cited by 40 · see citing works
Dagher, T., Dwyer, E., Baker, H. P., Kalidoss, S., & Strelzow, J. (2024) · Clinical Orthopaedics and Related Research · doi:10.1097/corr.0000000000003234 · Cited by 40 · see citing works
Self-citationTowards evidence-based practice 2.0: leveraging artificial intelligence in healthcare
Nilsen, P., Sundemo, D., Heintz, F., Neher, M., Nygren, J., Svedberg, P., & Petersson, L. (2024) · Frontiers in Health Services · doi:10.3389/frhs.2024.1368030 · Cited by 39 · see citing works
Physician EHR Time and Visit Volume Following Adoption of Team-Based Documentation Support
Apathy, N. C., Holmgren, A. J., & Cross, D. A. (2024) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2024.4123 · Cited by 34 · see citing works
Human–large language model collaboration in clinical medicine: a systematic review and meta-analysis
Wang, G., Zhang, K., Jiang, J., Wang, C., Bi, H., Liang, H., Qi, Z., Huang, Y., Li, Y., & Yang, X. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02382-2 · Cited by 42 · see citing works
Wu, Z., Gan, W., Xue, Z., Ni, Z., Zheng, X., & Zhang, Y. (2024) · JMIR Medical Education · doi:10.2196/52746 · Cited by 29 · see citing works
HealthBench: Evaluating Large Language Models Towards Improved Human Health
Arora, R. K., Wei, J., Hicks, R. S., Bowman, P., Quiñonero-Candela, J., Tsimpourlas, F., Sharman, M., Shah, M., Vallone, A., Beutel, A., et al. (2025) · arXiv · doi:10.48550/arxiv.2505.08775 · Cited by 28 · see citing works
“…generating differential diagnoses (Kanjee et al., 2023; McDuff et al., 2025), assisting with clinical documentation (Williams et al., 2024; Hartman et al., 2024; Baker et al., 2024), mental health treatment (Heinz et al., 2025), and radiology report generation (Tu et al., 2024; Tanno et al., 2025).”
Stephan, D., Bertsch, A., Schumacher, S., Puladi, B., Burwinkel, M., Al‐Nawas, B., Kämmerer, P. W., & Thiem, D. G. E. (2025) · Journal of Medical Internet Research · doi:10.2196/73337 · Cited by 33 · see citing works
The use of large language models in clinical documentation: A scoping review
Woo, B., Cato, K., Cho, H., You, S. B., & Song, J. (2025) · International Journal of Nursing Studies · doi:10.1016/j.ijnurstu.2025.105322 · Cited by 34 · see citing works
Blease, C., Kharko, A., Sanchez, C. G., Alderman, J. E., Kumpunen, S., Sundemo, D. S., & Torous, J. (2025) · Journal of Medical Internet Research · doi:10.2196/74428 · Cited by 28 · see citing works
Sezer, B., & Aydoğdu, T. (2025) · Applied Sciences · doi:10.3390/app15147778 · Cited by 23 · see citing works
Blease, C., Hagström, J., Sanchez, C. G., Kharko, A., McMillan, B., Gaab, J., Brulin, E., Locher, C., Hägglund, M., Riggare, S. S., et al. (2025) · Digital Health · doi:10.1177/20552076251394287 · Cited by 23 · see citing works
Perceptions and Use of Generative Artificial Intelligence in Medical Students: A Multicenter Survey
Tran, C., Hryciw, B. N., Moore, S. W., Chaput, A., & Seely, A. J. E. (2025) · Journal of Medical Education and Curricular Development · doi:10.1177/23821205251391969 · Cited by 22 · see citing works
Xue, Z., Zhang, Y., Gan, W., Wang, H., She, G., & Zheng, X. (2024) · Journal of Medical Internet Research · doi:10.2196/50882 · Cited by 22 · see citing works
The Labor Market Effects of Generative Artificial Intelligence
Hartley, J., Jolevski, F., Melo, V., & Moore, B. (2025) · doi:10.2139/ssrn.5136877 · Cited by 22 · see citing works
Jin, H., Guo, J., Lin, Q., Wu, S., Hu, W., & Li, X. (2024) · Frontiers in Digital Health · doi:10.3389/fdgth.2024.1456911 · Cited by 25 · see citing works
Chung, P., Swaminathan, A., Goodell, A. J., Kim, Y., Reincke, S. M., Han, L., Deverett, B., Sadeghi, M. A., Ariss, A.-B., Ghanem, M., et al. (2025) · NEJM AI · doi:10.1056/aidbp2500418 · Cited by 37 · see citing works
Cain, C. H., Davis, A. C., Broder, B., Chu, E., DeHaven, A. H., Domenigoni, A., Gin, N., Kapoor, A., Liu, V., MacLean, A., et al. (2025) · NEJM AI · doi:10.1056/aics2400977 · Cited by 19 · see citing works
Should Artificial Intelligence Be Used for Physician Documentation to Reduce Burnout?
Miao, J., Thongprayoon, C., & Cheungpasitporn, W. (2024) · Kidney360 · doi:10.34067/kid.0000000000000430 · Cited by 19 · see citing works
Kumar, A., Wang, H., Muir, K. W., Mishra, V., & Engelhard, M. (2025) · NEJM AI · doi:10.1056/aioa2400402 · Cited by 19 · see citing works
Vatsal, S., Dubey, H., & Singh, A. (2026) · IEEE Access · doi:10.1109/access.2026.3651218 · Cited by 21 · see citing works
Disambiguation of acronyms in clinical narratives with large language models
Kugic, A., Schulz, S., & Kreuzthaler, M. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae157 · Cited by 18 · see citing works
Wu, H., Yao, S., Bao, H., Guo, Y., Xu, C., & Ma, J. (2025) · The Knee · doi:10.1016/j.knee.2025.06.007 · Cited by 16 · see citing works
Surgeon, Trainee, or GPT ? A Blinded Multicentric Study of AI ‐Augmented Operative Notes
Hack, S., Attal, R., Locatelli, G., Scottà, G., Maniaci, A., Parisi, F. M., van der Poel, N. A., Daele, M. V., García‐Lliberós, A., Rodriguez‐Prado, C., et al. (2025) · The Laryngoscope · doi:10.1002/lary.70063 · Cited by 14 · see citing works
Comparison of Generative Artificial Intelligence and Predictive Artificial Intelligence
Harrington, L. (2024) · AACN Advanced Critical Care · doi:10.4037/aacnacc2024225 · Cited by 14 · see citing works
Unleashing ChatGPT: Redefining Technology Acceptance and Digital Transformation in Higher Education
Mourtajji, L., & Chiss, N. (2024) · Administrative Sciences · doi:10.3390/admsci14120325 · Cited by 17 · see citing works
Artificial intelligence in orthopaedic trauma
Tian, C., Gao, Y., Chen, R., Qin, S., Shi, L., & Rui, Y. (2024) · EngMedicine · doi:10.1016/j.engmed.2024.100020 · Cited by 13 · see citing works
Liu, J., Segal, K. R., Daher, M., Ozolin, J., Binder, W. D., Bergen, M. A., McDonald, C. L., Owens, B. D., & Antoci, V. (2025) · Injury · doi:10.1016/j.injury.2025.112297 · Cited by 13 · see citing works
General practitioners’ opinions of generative artificial intelligence in the UK: An online survey
Kharko, A., Sanchez, C. G., Hagström, J., Gaab, J., Locher, C., McMillan, B., Sundemo, D. S., & Blease, C. (2025) · Digital Health · doi:10.1177/20552076251360863 · Cited by 15 · see citing works
Generative artificial intelligence in medicine: a mixed-methods survey of UK general practitioners
Kharko, A., Locher, C., Torous, J. B., Rosch, S. A., Hägglund, M., Gaab, J., McMillan, B., Sundemo, D. S., Mandl, K. D., & Blease, C. (2025) · BMJ Digital Health & AI · doi:10.1136/bmjdhai-2025-000051 · Cited by 12 · see citing works
Stanton, N., Aziz, A., Jakhra, S., Wong, S., Morganstein, L., Bassett, P., Brewerton, M., Golchinheydari, S., Brogan, D., & Pushparajah, D. (2025) · BJPsych Bulletin · doi:10.1192/bjb.2025.10186 · Cited by 10 · see citing works
Rodenhouse, A. J., Davis, E., Burger, P., Nicandri, G. T., & DiGiovanni, B. F. (2025) · Journal of the American Academy of Orthopaedic Surgeons · doi:10.5435/jaaos-d-24-01321 · Cited by 10 · see citing works
Blease, C. (2025) · JMIR Mental Health · doi:10.2196/78663 · Cited by 10 · see citing works
Ambient AI reduces documentation time and enhances quality in a simulated inpatient setting
Bracken, A., Babu, A. R., Whelehan, S. P., Merghani, K., Sheehan, E. C., & Feeley, I. H. (2025) · The Surgeon · doi:10.1016/j.surge.2025.10.008 · Cited by 9 · see citing works
Artificial intelligence and physician burnout: A productivity paradox
Goodson, D. A., Garcia, B., Hogarth, M. A., & Tu, S.-P. (2025) · Learning Health Systems · doi:10.1002/lrh2.70013 · Cited by 10 · see citing works
Gün, M. (2025) · BMC Emergency Medicine · doi:10.1186/s12873-025-01303-y · Cited by 8 · see citing works
Choi, J. Y., & Yoo, T. K. (2026) · Discover Health Systems · doi:10.1007/s44250-026-00343-w · Cited by 10 · see citing works
Knott, M., Krebs, M., & Kerscher, A. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1761641 · Cited by 8 · see citing works
Goldie, J., Dennis, S., Hipgrave, L., & Coleman, A. (2025) · JMIR Human Factors · doi:10.2196/71065 · Cited by 7 · see citing works
Artificial intelligence in emergency medicine: a narrative review
Rego, A., Arango-Ibañez, J. P., Taylor, R. A., Smith, M. E., Jones, D. D., Pelletier, J., Colletti, J. E., Gottlieb, M. A., & Long, B. (2026) · The American Journal of Emergency Medicine · doi:10.1016/j.ajem.2026.01.028 · Cited by 7 · see citing works
Generative Artificial Intelligence Summaries to Facilitate Emergency Department Handoff
Genes, N. G., Simon, G. E., Koziatek, C. A., Kim, J. G., Woo, K.-M. C., Dahn, C., Chan, L., & Wiesenfeld, B. M. (2025) · Applied Clinical Informatics · doi:10.1055/a-2681-5008 · Cited by 6 · see citing works
Chiang, Y.-L., Yang, K.-F., Su, P.-Y., Tsai, S., & Liang, K.-L. (2025) · Applied Clinical Informatics · doi:10.1055/a-2707-2959 · Cited by 6 · see citing works
The Effect of Ambient Listening Technology on the Patient Experience
Patel, K. N., Stout, M. M., Solis, M. A., Riyahi, N., Holland, C. J., Vohra-Khullar, P. D., Moore, M. A., & Dbouk, R. H. (2026) · Applied Clinical Informatics · doi:10.1055/a-2807-4098 · Cited by 6 · see citing works
Evaluation of a Large Language Model’s Ability to Assist in an Orthopedic Hand Clinic
Kotzur, T. M., Singh, A., Parker, J., Peterson, B. C., Sager, B. W., Rose, R., Corley, F. G., & Brady, C. I. (2024) · Hand · doi:10.1177/15589447241257643 · Cited by 6 · see citing works
Ghanem, D. (2024) · International Journal of Surgery · doi:10.1097/js9.0000000000002130 · Cited by 6 · see citing works
The Future of the Psychiatrist
Potash, J. B., McClanahan, A., Davidson, J., Butler, W., Carroll, N., Ruble, A. E., Yaden, M. E., King, D. R., Torous, J. B., Zandi, P. P., et al. (2025) · Psychiatric Research and Clinical Practice · doi:10.1176/appi.prcp.20240130 · Cited by 9 · see citing works
Artificial intelligence: Revolutionizing pediatric emergency care - A narrative review
Saha, A., Shukla, A., & Bhaskar, V. (2025) · International Journal of Critical Illness and Injury Science · doi:10.4103/ijciis.ijciis_24_25 · Cited by 5 · see citing works
Evaluating the accuracy of ChatGPT model versions for giving care-seeking advice
Kopka, M., He, L., & Feufel, M. A. (2026) · Communications Medicine · doi:10.1038/s43856-026-01466-0 · Cited by 5 · see citing works
Blease, C., Hagström, J., Sanchez, C. G., Kharko, A., McMillan, B., Gaab, J., Brulin, E., Locher, C., Hägglund, M., Riggare, S. S., et al. (2025) · Research Square · doi:10.21203/rs.3.rs-6196250/v1 · Cited by 5 · see citing works
One shot at trust: building credible evidence for medical artificial intelligence
Kouzy, R., Hong, J. C., & Bitterman, D. S. (2025) · The Lancet Digital Health · doi:10.1016/j.landig.2025.100883 · Cited by 4 · see citing works
ChatGPT versus expert arthroplasty surgeons in total knee arthroplasty patient counseling
Liu, J., Daher, M., Laperche, J. M., Gilreath, N., Testa, E. J., El-Othamni, M. M., Barrett, T. J., & Antoci, V. (2025) · The Knee · doi:10.1016/j.knee.2025.03.005 · Cited by 4 · see citing works
Sanchez, C. G., Kharko, A., Hägglund, M., Riggare, S. S., & Blease, C. (2025) · JMIR Research Protocols · doi:10.2196/73602 · Cited by 4 · see citing works
Papneja, H., & Devaraj, S. (2025) · Journal of Operations Management · doi:10.1002/joom.70026 · Cited by 5 · see citing works
Artificial intelligence in occupational therapy documentation: Chatbot vs. Occupational Therapists
Lee, S.-A., & Park, J. (2025) · Digital Health · doi:10.1177/20552076251386657 · Cited by 5 · see citing works
Akman, E., Aksu, D., & Can, Y. (2024) · Afyon Kocatepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi · doi:10.33707/akuiibfd.1463116 · Cited by 4 · see citing works
AI in flow cytometry: Current applications and future directions
Yue, A., Brinkman, R. R., Nash, V., Junker, F., Bogdanoski, G., Divekar, A., Tyznik, A. J., Špidlen, J., Kern, W., Pétriz, J., et al. (2025) · Cytometry Part B Clinical Cytometry · doi:10.1002/cyto.b.22255 · Cited by 6 · see citing works
Gaddis, J. M., Arellano, E., Martin, B. C., Alsabawi, Y., Salgado‐Flores, M., South, C. C., Castañeda, P. G., & Wells, J. E. (2025) · Journal of Experimental Orthopaedics · doi:10.1002/jeo2.70457 · Cited by 3 · see citing works
Gorgos, P., Ternell, K. H., Hammarstrand, C., Wallmon, A., Brogren, E., Björkman, A., & Horváth, A. (2025) · Hand surgery & rehabilitation · doi:10.1016/j.hansur.2025.102530 · Cited by 3 · see citing works
AI in Pediatric Spine Care: Clinical, Research, and Ethical Considerations
Nugraha, H. K., Rasmussen, A. P., Mulford, K. L., Yang, L., Wyles, C. C., & Larson, A. N. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14228115 · Cited by 3 · see citing works
Zhang, Z., Scroggins, J. K., Harkins, S., Hulchafo, I. I., Moen, H., Tadiello, M., Barcelona, V., & Topaz, M. (2025) · Nursing Outlook · doi:10.1016/j.outlook.2025.102472 · Cited by 3 · see citing works
Blease, C., Kharko, A., Locher, C., Torous, J. B., Rosch, S. A., Hägglund, M., Gaab, J., McMillan, B., Sundemo, D. S., & Mandl, K. D. (2024) · doi:10.2196/preprints.65318 · Cited by 4 · see citing works
Artificial Intelligence for Regional Anesthesiology and Acute Pain Medicine in the 21st Century
Schroeder, K. M., Nanda, M., Mariano, E. R., Gupta, R. K., Maniker, R. B., & Elkassabany, N. M. (2025) · Anesthesiology Clinics · doi:10.1016/j.anclin.2025.02.009 · Cited by 2 · see citing works
Tarapore, R., Gupta, S., Means, K. R., & Giladi, A. M. (2025) · Journal of Hand Surgery Global Online · doi:10.1016/j.jhsg.2025.100822 · Cited by 2 · see citing works
Lei, Y.-H., Chen, C., & Shen, C. (2025) · Medical Education Online · doi:10.1080/10872981.2025.2602788 · Cited by 2 · see citing works
Odisho, A. Y., Liu, A. W., Pace, W. A., Carlisle, M. N., Krumm, R., Cowan, J. E., Carroll, P. R., & Cooperberg, M. R. (2026) · JCO Clinical Cancer Informatics · doi:10.1200/cci-24-00334 · Cited by 2 · see citing works
Çil, M. A., Karataş, N., Mustafaoglu, O., & Sevinç, C. (2026) · BMC Nephrology · doi:10.1186/s12882-026-04764-w · Cited by 2 · see citing works
Thind, B. S., Javidi, D., & Schwartz, L. M. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1830254 · Cited by 3 · see citing works
Koli, H. V., Thaker, D. J., & Khengar, J. (2025) · International Conference on Trends in Engineering Systems and Technologies (ICTEST) · doi:10.1109/ictest64710.2025.11042869 · Cited by 2 · see citing works
Lin, Y., Wang, Y., Deng, Y., Xu, D., & Wang, M. (2024) · doi:10.2196/preprints.60412 · Cited by 1 · see citing works
Rachmat, H., Riza, H., & Abidin, T. F. (2024) · International Conference on Informatics and Computing (ICIC) · doi:10.1109/icic64337.2024.10956296 · Cited by 1 · see citing works
Artificial Intelligence in Hand Surgery: The Future is Upon Us
Crook, B. S., Hurley, E. T., Richard, M. J., Mithani, S. K., & Pidgeon, T. S. (2025) · Journal of Hand Surgery Global Online · doi:10.1016/j.jhsg.2025.100793 · Cited by 1 · see citing works
Li, X., Du, J., Liu, Y., Yin, H., & Liu, H. (2026) · Big Data Mining and Analytics · doi:10.26599/bdma.2025.9020086 · Cited by 1 · see citing works
Compliance with the CONSORT AI Extension in Orthopaedic Randomized Controlled Trials
Strasser, T., Baker, C., Cheminant, J. R., Han, B., Canumay, S., & Schlechter, J. A. (2026) · Journal of Bone and Joint Surgery · doi:10.2106/jbjs.25.01439 · Cited by 1 · see citing works
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Hao, J., Yao, Z., Remis, A., & Yu, X. (2025) · Journal of Evidence-Based Medicine · doi:10.1111/jebm.70074 · Cited by 1 · see citing works
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Kopka, M., He, L., & Feufel, M. A. (2025) · medRxiv · doi:10.1101/2025.05.13.25327519 · Cited by 1 · see citing works
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Hacişahinoğulları, R., & Köroğlu, E. Ö. (2024) · Gümüşhane Üniversitesi Sağlık Bilimleri Dergisi · doi:10.37989/gumussagbil.1369259
Goldie, J.-M. D., Dennis, S. J., Hipgrave, L., & Coleman, A. (2025) · doi:10.2196/preprints.71065
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Cornelius, J., Knitza, J., Hack, J., Pavlović, M., & Kühn, S. (2025) · Die Unfallchirurgie · doi:10.1007/s00113-025-01581-y
Pidgeon, T. S. (2025) · The Journal Of Hand Surgery · doi:10.1016/j.jhsa.2025.02.013
Effect of an AI-powered clinical workflow tool on CDI queries
Alvarez, F. N., & Koppel, R. (2025) · Journal of Translational Research · doi:10.1080/29947448.2025.2520761
Sharma, M., Ganie, K. A., Panwar, P., & Arti (2025) · International Journal of Latest Technology in Engineering Management & Applied Science · doi:10.51583/ijltemas.2025.1412000008
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Strelzow, J., & Ghert, M. A. (2025) · Journal of Bone and Joint Surgery · doi:10.2106/jbjs.25.01376
Self-citationWang, S.-P., & Liu, Y.-L. (2026) · Digital Health · doi:10.1177/20552076261416808
State of the Art of Knowledge on Applications of Artificial Intelligence in Urology
Reese, V., Santare, J. A., Lee, R., Maddahi, Y., & Verges, D. P. (2025) · Artificial intelligence · doi:10.5772/intechopen.1011663
Burstein, D. S., Schwartz, N., Ganguly, A., Hanak, M. A., Zimmermann, L. J., & Keshavarzian, A. (2026) · Diagnosis · doi:10.1515/dx-2025-0088
Lee, J. H., Ryu, B., & Kim, Y. K. (2026) · Research Square · doi:10.21203/rs.3.rs-8719534/v1
- Authors
- Baker, H. P., Dwyer, E., Kalidoss, S., Hynes, K., Wolf, J., & Strelzow, J. A.
- Affiliations
- University of Chicago (US)
- Corresponding author
- Baker, Hayden P.
- Venue
- Journal of the American Academy of Orthopaedic Surgeons
- Published
- 2023-11-17
- Volume
- 32
- Issue
- 3
- Pages
- 123-129
- ISSN
- 1067-151X (ISSN-L)
- 1940-5480
- OpenAlex
- W4388753652
- Cited by
- 115
- References
- 7
- Citations by year
- 2026: 30 · 2025: 56 · 2024: 29
- FWCI
- 4.59
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the American Academy of Orthopaedic Surgeons (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Nursing › Issues, ethics and legal aspects › Nursing Diagnosis and Documentation
- Keywords
- ChatGPT, clinical documentation, history of present illness, artificial intelligence in healthcare, dictation, typing, documentation quality, standardized patients, orthopaedic residents, medical students
- MeSH terms
- Artificial Intelligence, Communication, Documentation, Health Facilities, Humans, Surgeons
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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.
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