Development and Validation of an Artificial Intelligence System to Optimize Clinician Review of Patient Records
Open accessAbstract
Importance: Physicians are required to work with rapidly growing amounts of medical data. Approximately 62% of time per patient is devoted to reviewing electronic health records (EHRs), with clinical data review being the most time-consuming portion. Objective: To determine whether an artificial intelligence (AI) system developed to organize and display new patient referral records would improve a clinician's ability to extract patient information compared with the current standard of care. Design, Setting, and Participants: In this prognostic study, an AI system was created to organize patient records and improve data retrieval. To evaluate the system on time and accuracy, a nonblinded, prospective study was conducted at a single academic medical center. Recruitment emails were sent to all physicians in the gastroenterology division, and 12 clinicians agreed to participate. Each of the clinicians participating in the study received 2 referral records: 1 AI-optimized patient record and 1 standard (non-AI-optimized) patient record. For each record, clinicians were asked 22 questions requiring them to search the assigned record for clinically relevant information. Clinicians reviewed records from June 1 to August 30, 2020. Main Outcomes and Measures: The time required to answer each question, along with accuracy, was measured for both records, with and without AI optimization. Participants were asked to assess overall satisfaction with the AI system, their preferred review method (AI-optimized vs standard), and other topics to assess clinical utility. Results: Twelve gastroenterology physicians/fellows completed the study. Compared with standard (non-AI-optimized) patient record review, the AI system saved first-time physician users 18% of the time used to answer the clinical questions (10.5 [95% CI, 8.5-12.6] vs 12.8 [95% CI, 9.4-16.2] minutes; P = .02). There was no significant decrease in accuracy when physicians retrieved important patient information (83.7% [95% CI, 79.3%-88.2%] with the AI-optimized vs 86.0% [95% CI, 81.8%-90.2%] without the AI-optimized record; P = .81). Survey responses from physicians were generally positive across all questions. Eleven of 12 physicians (92%) preferred the AI-optimized record review to standard review. Despite a learning curve pointed out by respondents, 11 of 12 physicians believed that the technology would save them time to assess new patient records and were interested in using this technology in their clinic. Conclusions and Relevance: In this prognostic study, an AI system helped physicians extract relevant patient information in a shorter time while maintaining high accuracy. This finding is particularly germane to the ever-increasing amounts of medical data and increased stressors on clinicians. Increased user familiarity with the AI system, along with further enhancements in the system itself, hold promise to further improve physician data extraction from large quantities of patient health records.
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In an earlier study, twelve GI physicians utilized an institutionally developed AI tool to help with CDM (AI-Optimized) compared to standard processes.
“In an earlier study, twelve GI physicians utilized an institutionally developed AI tool to help with CDM (AI-Optimized) compared to standard processes. 16”
From The use of an artificial intelligence platform OpenEvidence to augment clinical decision-making for primary care physicians (2025) · cites this paper as [16]
The Methods (Study Design; page not available in our copy) report that 12 gastroenterology clinicians each reviewed an AI-optimized record and a standard record using the study’s AI system. They answered questions designed to test extraction of information from referral records; the study did not test clinical decision-making (CDM) itself. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe AI-optimized tool saved physicians 18% of the time (P < .02) required to answer clinical questions with no significant decrease in accuracy in CDM compared to standard processes.
“The AI-optimized tool saved physicians 18% of the time (P < .02) required to answer clinical questions with no significant decrease in accuracy in CDM compared to standard processes. 16”
From The use of an artificial intelligence platform OpenEvidence to augment clinical decision-making for primary care physicians (2025) · cites this paper as [16]
The Results (Association of AI Optimization With Information Extraction Time) report 18% less time to answer 22 clinical questions, but give P = .02, not P < .02. The Results (Association of AI Optimization With Clinician Accuracy) report comparable accuracy on those questions (P = .81); they do not directly measure accuracy in clinical decision-making. (LLM, conf=0.88)
Experimental — accuracy not yet measured
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Cites
Allocation of Physician Time in Ambulatory Practice: A Time and Motion Study in 4 Specialties
Sinsky, C., Colligan, L., Li, L., Prgomet, M., Reynolds, S., Goeders, L., Westbrook, J., Tutty, M., & Blike, G. (2016) · Annals of Internal Medicine · doi:10.7326/m16-0961 · Cited by 1,494 · see citing works
Rajpurkar, P., Irvin, J., Ball, R. L., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C. P., et al. (2018) · PLOS Medicine · doi:10.1371/journal.pmed.1002686 · Cited by 1,449 · see citing works
Self-citationShanafelt, T. D., Dyrbye, L. N., Sinsky, C., Hasan, O., Satele, D., Sloan, J., & West, C. P. (2016) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2016.05.007 · Cited by 1,086 · see citing works
Human-computer collaboration for skin cancer recognition
Tschandl, P., Rinner, C., Apalla, Z., Argenziano, G., Codella, N., Halpern, A., Janda, M., Lallas, A., Longo, C., Malvehy, J., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0942-0 · Cited by 1,024 · see citing works
Arndt, B. G., Beasley, J. W., Watkinson, M. D., Temte, J. L., Tuan, W.-J., Sinsky, C. A., & Gilchrist, V. J. (2017) · The Annals of Family Medicine · doi:10.1370/afm.2121 · Cited by 972 · see citing works
Clinical information extraction applications: A literature review
Wang, Y., Wang, L., Rastegar-Mojarad, M., Moon, S., Shen, F., Afzal, N., Liu, S., Zeng, Y., Mehrabi, S., Sohn, S., et al. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.11.011 · Cited by 910 · see citing works
Electronic Health Records: Then, Now, and in the Future
Evans, R. S. (2016) · Yearbook of Medical Informatics · doi:10.15265/iys-2016-s006 · Cited by 816 · see citing works
Friedberg, M., Chen, P., Van Busum, K., Aunon, F., Pham, C., Caloyeras, J., Mattke, S., Pitchforth, E., Quigley, D., Brook, R., et al. (2014) · RAND Health Quarterly · doi:10.7249/rhq-03-04-01 · Cited by 707 · see citing works
MedEx: a medication information extraction system for clinical narratives
Xu, H., Stenner, S. P., Doan, S., Johnson, K. B., Waitman, L. R., & Denny, J. C. (2010) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m3378 · Cited by 532 · see citing works
Electronic medical records and physician stress in primary care: results from the MEMO Study
Babbott, S., Manwell, L. B., Brown, R., Montague, E., Williams, E., Schwartz, M., Hess, E., & Linzer, M. (2014) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-001875 · Cited by 366 · see citing works
Association of Electronic Health Record Design and Use Factors With Clinician Stress and Burnout
Kroth, P. J., Morioka-Douglas, N., Veres, S., Babbott, S. F., Poplau, S., Qeadan, F., Parshall, C., Corrigan, K., & Linzer, M. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.9609 · Cited by 340 · see citing works
Impact of a deep learning assistant on the histopathologic classification of liver cancer
Kiani, A., Uyumazturk, B., Rajpurkar, P., Wang, A., Gao, R., Jones, E., Yu, Y., Langlotz, C. P., Ball, R. L., Montine, T. J., et al. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0232-8 · Cited by 325 · see citing works
Self-citationAre Electronic Medical Records Helpful for Care Coordination? Experiences of Physician Practices
O’Malley, A. S., Grossman, J. M., Cohen, G. R., Kemper, N., & Pham, H. H. (2009) · Journal of General Internal Medicine · doi:10.1007/s11606-009-1195-2 · Cited by 298 · see citing works
Deep Learning–Assisted Diagnosis of Cerebral Aneurysms Using the HeadXNet Model
Park, A., Chute, C., Rajpurkar, P., Lou, J., Ball, R. L., Shpanskaya, K., Jabarkheel, R., Kim, L. H., McKenna, E., Tseng, J., et al. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.5600 · Cited by 258 · see citing works
Self-citationPhysician Time Spent Using the Electronic Health Record During Outpatient Encounters
Overhage, J. M., & McCallie, D. P. (2020) · Annals of Internal Medicine · doi:10.7326/m18-3684 · Cited by 249 · see citing works
The influence of electronic health record use on physician burnout: cross-sectional survey
Tajirian, T., Stergiopoulos, V., Strudwick, G., Sequeira, L., Sanches, M., Kemp, J., Ramamoorthi, K., Zhang, T., & Jankowicz, D. (2020) · Journal of Medical Internet Research · doi:10.2196/19274 · Cited by 241 · see citing works
Rajpurkar, P., O’Connell, C., Schechter, A., Asnani, N., Li, J., Kiani, A., Ball, R. L., Mendelson, M., Maartens, G., van Hoving, D. J., et al. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00322-2 · Cited by 137 · see citing works
Self-citationSeven years after Meaningful Use: Physicians’ and nurses’ experiences with electronic health records
Rathert, C., Porter, T. H., Mittler, J., & Fleig-Palmer, M. M. (2017) · Health Care Management Review · doi:10.1097/hmr.0000000000000168 · Cited by 108 · see citing works
The electronic elephant in the room: Physicians and the electronic health record
Kroth, P. J., Morioka-Douglas, N., Veres, S., Pollock, K., Babbott, S. F., Poplau, S., Corrigan, K., & Linzer, M. (2018) · JAMIA Open · doi:10.1093/jamiaopen/ooy016 · Cited by 104 · see citing works
A frame semantic overview of NLP-based information extraction for cancer-related EHR notes
Datta, S., Bernstam, E. V., & Roberts, K. E. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103301 · Cited by 92 · see citing works
Lucila Ohno-machado, A., Nadkarni, P. M., & Johnson, K. K. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-002214 · Cited by 47 · see citing works
Internal Medicine Resident Computer Usage: An Electronic Audit of an Inpatient Service
Ouyang, D., Chen, J. H., Hom, J., & Chi, J. (2016) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2015.6831 · Cited by 47 · see citing works
Clinical Information Extraction at the CLEF eHealth Evaluation lab 2016.
Aurélie Névéol, A., Cohen, K. B., Grouin, C., Thierry Hamon, C., Lavergne, T., Kelly, L., Goeuriot, L., Rey, G., Robert, A., Tannier, X., et al. (2016) · PubMed · Cited by 43 · see citing works
Chi, J., Bentley, J., Kugler, J., & Chen, J. H. (2019) · PLoS ONE · doi:10.1371/journal.pone.0221300 · Cited by 32 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Heterogeneity and predictors of the effects of AI assistance on radiologists
Yu, F., Moehring, A., Banerjee, O., Salz, T., Agarwal, N., & Rajpurkar, P. (2024) · Nature Medicine · doi:10.1038/s41591-024-02850-w · Cited by 200 · see citing works
Self-citationPaying for artificial intelligence in medicine
Parikh, R. B., & Helmchen, L. A. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00609-6 · Cited by 132 · see citing works
Yim, D., Khuntia, J., Parameswaran, V., & Meyers, A. D. (2024) · JMIR Medical Informatics · doi:10.2196/52073 · Cited by 129 · see citing works
Hurt, 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
“2 Several specific studies have evaluated the role of AI tools in specialty practices outside the PCCs. 15,16 A recent study evaluated urologists’ use of an AI tool (Siemens AI Pathway Companion; Siemans Healthcare, Germany) to assist in CDM for ten patients with prostate cancer.”
Chen, Y., Lehmann, C. U., & Malin, B. (2024) · Journal of Medical Internet Research · doi:10.2196/60258 · Cited by 75 · see citing works
Taylor, R. A., Sangal, R. B., Smith, M. E., Haimovich, A. D., Rodman, A., Iscoe, M. S., Pavuluri, S. K., Rose, C., Janke, A. T., Wright, D. S., et al. (2025) · Academic Emergency Medicine · doi:10.1111/acem.15066 · Cited by 62 · see citing works
Flowing through laboratory clinical data: the role of artificial intelligence and big data
Padoan, A., & Plebani, M. (2022) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm-2022-0653 · Cited by 42 · see citing works
Artificial intelligence-enhanced patient evaluation: bridging art and science
Oikonomou, E. K., & Khera, R. (2024) · European Heart Journal · doi:10.1093/eurheartj/ehae415 · Cited by 34 · see citing works
Wang, X., Si, J., Li, Y., Tse, P., Zhang, G., Wang, X., Ren, J., Xu, J., Sun, J., & Yao, X. (2025) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-025-01819-0 · Cited by 30 · see citing works
Greenwald, S. D., Chamoun, G. F., Chamoun, N. G., Clain, D., Hong, Z., Jordan, R., Manberg, P. J., Maheshwari, K. K., & Sessler, D. I. (2022) · Anesthesiology · doi:10.1097/aln.0000000000004380 · Cited by 29 · see citing works
Artificial Intelligence as a Potential Catalyst to a More Equitable Cancer Care
García-Saisó, S., Martí, M., Pesce, K. A., Luciani, S., Mújica, Ó. J., Hennis, A. J. M., & D'Agostino, M. J. (2024) · JMIR Cancer · doi:10.2196/57276 · Cited by 27 · see citing works
Multimodal generative AI for interpreting 3D medical images and videos
Lee, J.-O., Zhou, H.-Y., Berzin, T. M., Sodickson, D. K., & Rajpurkar, P. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01649-4 · Cited by 25 · see citing works
Self-citationAh-Thiane, L., Heudel, P., Campone, M., Robert, M., Brillaud-Meflah, V., Rousseau, C., Blanc‐Onfroy, M. L., Tomaszewski, F., Supiot, S., Perennec, T., et al. (2025) · JCO Clinical Cancer Informatics · doi:10.1200/cci-24-00230 · Cited by 28 · see citing works
Enslin, S., & Kaul, V. (2024) · Gastrointestinal Endoscopy Clinics of North America · doi:10.1016/j.giec.2024.09.003 · Cited by 21 · see citing works
Artificial intelligence in emergency medicine: a primer for the nonexpert
Smith, M. E., Zalesky, C. C., Lee, S., Gottlieb, M., Adhikari, S., Goebel, M., Wegman, M., Garg, N., & Lam, S. H. F. (2025) · JACEP Open · doi:10.1016/j.acepjo.2025.100051 · Cited by 18 · see citing works
Enhancing serious illness communication using artificial intelligence
Chua, I. S., Ritchie, C. S., & Bates, D. W. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00556-2 · Cited by 18 · see citing works
Park, E., Watson, H. I., Mehendale, F. V., & O’Neil, A. Q. (2022) · JMIR Medical Informatics · doi:10.2196/39616 · Cited by 15 · see citing works
User-centered design of a machine learning dashboard for prediction of postoperative complications
Fritz, B. A., Pugazenthi, S., Budelier, T. P., Tellor Pennington, B. R., King, C. R., Avidan, M. S., & Abraham, J. (2024) · Anesthesia & Analgesia · doi:10.1213/ane.0000000000006577 · Cited by 15 · see citing works
Scott, I. A., Reddy, S., Kelly, T., Miller, T., & van der Vegt, A. H. (2025) · The Medical Journal of Australia · doi:10.5694/mja2.70057 · Cited by 15 · see citing works
Opening the Precision Diabetes Care through Digital Healthcare
Lee, J., Yu, J. S., & Yoon, K. (2023) · Diabetes & Metabolism Journal · doi:10.4093/dmj.2022.0386 · Cited by 12 · see citing works
Baron, R. J. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.18298 · Cited by 11 · see citing works
Improving diagnosis in health care: laboratory medicine
Lippi, G., Jackson, B. R., & Plebani, M. (2025) · Diagnosis · doi:10.1515/dx-2025-0052 · Cited by 9 · see citing works
Rammohan, R., Joy, M. V., Magam, S. G., Natt, D., Magam, S. R., Pannikodu, L. E., Desai, J., Akande, O., Bunting, S., Yost, R. M., et al. (2024) · Cureus · doi:10.7759/cureus.51848 · Cited by 9 · see citing works
Fahmy, A. (2025) · Premier Journal of Public Health · doi:10.70389/pjph.100021 · Cited by 3 · see citing works
A Large Language Model Assistant for Summarizing Hepatology Referral Documents
Shroff, H., Shankar, A., Baron, A., Yoxheimer, L., & Fix, O. K. (2026) · The American Journal of Gastroenterology · doi:10.14309/ajg.0000000000003905 · Cited by 7 · see citing works
AI‐Driven Innovations in Healthcare: Balancing Technology With Ethics
Garno, Z., Elkari, B., & Ourabah, L. (2025) · World Medical & Health Policy · doi:10.1002/wmh3.70031 · 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
Xu, Y., Jia, H., Wang, M., Feng, J., Xu, X., Wang, H., Chen, J., Zheng, Z., Xiaoyan, Y., Shen, Y., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02170-4 · Cited by 7 · see citing works
User-Centered Design in AI Applications: A Systematic Literature Review
Wongso, B., Lienaka, K. N., Firstian, V., & Magdalena, Y. (2024) · International Conference on Information Management and Technology (ICIMTech) · doi:10.1109/icimtech63123.2024.10780823 · Cited by 6 · see citing works
Artificial intelligence in cardiology: did it take off?
Yeo, K. K. (2023) · Russian Journal for Personalized Medicine · doi:10.18705/2782-3806-2022-2-6-16-22 · Cited by 6 · see citing works
Keng, C., DiGiorgio, A. M., Ehrenfeld, J. M., Spear, J., & Miller, B. J. (2025) · Journal of Medical Systems · doi:10.1007/s10916-025-02265-1 · Cited by 6 · see citing works
Kusztos, V. E., & Simonetto, D. A. (2025) · Journal of Clinical and Experimental Hepatology · doi:10.1016/j.jceh.2025.102579 · Cited by 5 · see citing works
AI bias in lung cancer radiotherapy
Ding, K., Forbes, S., Ma, F., Luo, G., Zhou, J., & Qi, Y. (2024) · Exploration of Digital Health Technologies · doi:10.37349/edht.2024.00030 · Cited by 5 · see citing works
Hershenhouse, J. S., Gill, K. S., & Nabhani, J. A. (2025) · Current Opinion in Urology · doi:10.1097/mou.0000000000001282 · Cited by 4 · see citing works
Vidula, N., & Peppercorn, J. M. (2023) · JCO Oncology Practice · doi:10.1200/op.23.00500 · Cited by 4 · see citing works
Gomase, V. S., VISOKAR, D. L., & Chopade, V. V. (2025) · Current Drug Discovery Technologies · doi:10.2174/0115701638361312250402125556 · Cited by 3 · see citing works
Artificial intelligence for epilepsy decision support
Loushy, I., & Sperling, M. R. (2025) · Epilepsia · doi:10.1111/epi.18709 · Cited by 3 · see citing works
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Ozkaynak, M., Shan, Z., & Wu, D. T. Y. (2025) · Cognitive informatics in biomedicine and healthcare · doi:10.1007/978-3-031-82971-0_6 · Cited by 2 · see citing works
Medical applications of artificial intelligence
Pradhan, B. K., Biswas, D., Neelapu, B. C., Sivaraman, J., & Pal, K. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-19073-5.00012-4 · Cited by 2 · see citing works
Implications of using conversational robots (chatbots) in uro-oncology
NIEVA-POSSO, D. A., Nieva‐Posso, D. A., & García‐Perdomo, H. A. (2024) · Canadian Urological Association Journal · doi:10.5489/cuaj.8762 · Cited by 2 · see citing works
Emerging clinical applications of large language models in emergency medicine
Herries, J. (2024) · Emergency Medicine Australasia · doi:10.1111/1742-6723.14457 · Cited by 2 · see citing works
Comparative Performance of Large Language Models in Ophthalmology Referral Triage
Cardoso-Teixeira, P., Ambrósio, J. A., Garcia, M., Chibante-Pedro, J., & Figueiredo, L. (2026) · Cureus · doi:10.7759/cureus.102060 · Cited by 1 · see citing works
On-Device Artificial Intelligence for Image Analysis in Resource-Limited Settings: A Review
Xie, C., An, J. E., Bae, H.-J., Yun, E. S., Ko, K.-C., Jeong, D. S., & Kwon, O. S. (2025) · Applied Science and Convergence Technology · doi:10.5757/asct.2026.35.1.1 · Cited by 1 · see citing works
Scholz, F., Sahakian, T. S., GÜVEN, N., Renkema, M., Shulzhenko, E., Cuijpers, C., Schouten, G., Wouters, E., & de Vries, E. (2026) · The International Journal of Human Resource Management · doi:10.1080/09585192.2026.2711067 · Cited by 1 · see citing works
Data-Driven Clinical Decision Support Systems Theory and Research
Noteboom, C., Zeng, D., Sutrave, K., Behrens, A., Godasu, R., & Chauhan, A. (2023) · IGI Global eBooks · doi:10.4018/978-1-7998-9220-5.ch081
Artificial Intelligence: Revolutionizing the Medicolegal Field
Brigham, C. R., Buttar, W. A., Bucksbaum, M., & Talmage, J. B. (2023) · AMA guides newsletter · doi:10.1001/amaguidesnewsletters.2023.sepoct01
Boas, P. M. V., Vieira, L. J. E. D. S., da Silva, G. B., Lira, G. V., & de Oliveira, J. G. R. (2024) · Revista da Associação Médica Brasileira · doi:10.1590/1806-9282.20231136
Tíscar-González, V., & Cuevas, J. C.-D. L. (2024) · Enfermería Clínica · doi:10.1016/j.enfcli.2024.06.001
Transforming the health system through data: access and governance as pivotal elements
Tíscar-González, V., & Cuevas, J. C.-D. L. (2024) · Enfermería Clínica (English Edition) · doi:10.1016/j.enfcle.2024.06.002
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Kümpers, P., Casu, S., & Kumle, B. (2024) · Notaufnahme up2date · doi:10.1055/a-2300-6235
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Hoverson, J., Buch, C. A., Ulloa-Rodriguez, J. C., Governor, S., Pak, S., & Otchere, P. (2025) · Oncotarget · doi:10.18632/oncotarget.28738
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Jumbo, U. E., Otto, C., Rickards, J., MacLeod, J., Olson, J. M. G., & Brown, S.-A. N. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-29829-5.00010-4
Yang, B., Lee, J., Tsai, T. K., Sattler, A. L., Shah, S., Smith, M. A., & Lin, S. (2025) · Journal of General Internal Medicine · doi:10.1007/s11606-025-09716-y
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Safarov, R., Wippermann, J., & Wacker, M. (2026) · Zeitschrift für Herz- Thorax- und Gefäßchirurgie · doi:10.1007/s00398-026-00763-z
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Pérez-Ferrer, A., Díaz, E. G., Sánchez, J. D. V., Luengo, A. R., Rodríguez, M. Á. P., Báez, Y. L., Filippini, S. E. D., García, R. H., García, R. G., Ogea, M. A. C. C., et al. (2026) · European Journal of Anaesthesiology · doi:10.1097/eja.0000000000002418
Roopa, V., Abiman, S., Dhanushkumar, M., Haridharan, S., & R, J. A. J. (2026) · International conference Computer Networks and Inventive Communication Technologies · doi:10.1109/iccnct68477.2026.11590399
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Turner, M., Nguyen, S., Clark, D., & Wilson, E. (2021) · Journal of Health Informatics and Digital Systems · doi:10.68159/e431890076
- Authors
- Chi, E. A., Chi, G., Tsui, C. T., Jiang, Y., Jarr, K., Kulkarni, C. V., Zhang, M., Long, J., Ng, A. Y., Rajpurkar, P., & Sinha, S. R.
- Affiliations
- Stanford University (US)
- Stanford Medicine (US)
- Corresponding author
- Rajpurkar, Pranav
- Sinha, Sidhartha R.
- Venue
- JAMA Network Open
- Publisher
- American Medical Association (AMA)
- Published
- 2021-07-23
- Volume
- 4
- Issue
- 7
- Pages
- e2117391
- ISSN
- 2574-3805 (ISSN-L)
- PMC
- PMC8303101
- OpenAlex
- W3184771626
- Cited by
- 64
- References
- 24
- Citations by year
- 2026: 10 · 2025: 22 · 2024: 17 · 2023: 8 · 2022: 5 · 2021: 2
- FWCI
- 6.97
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JAMA Network Open (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- artificial intelligence, electronic health records, clinical data retrieval, clinical decision support, gastroenterology, information accuracy, learning curve, physician satisfaction, natural language processing, clinical workflow efficiency
- MeSH terms
- Academic Medical Centers, User-Centered Design, Adult, Artificial Intelligence, Female, Humans, Job Satisfaction, Male, Medical Records, Middle Aged, Physicians, Prospective Studies, Referral and Consultation, Task Performance and Analysis, Time Factors, Information Storage and Retrieval, Workload
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