Exploring how feedback reflects entrustment decisions using artificial intelligence
Open accessAbstract
Context: Clinical supervisors make judgements about how much to trust learners with critical activities in patient care. Such decisions mediate trainees' opportunities for learning and competency development and thus are a critical component of education. As educators apply entrustment frameworks to assessment, it is important to determine how narrative feedback reflecting entrustment may also address learners' educational needs. Methods: In this study, we used artificial intelligence (AI) and natural language processing (NLP) to identify characteristics of feedback tied to supervisors' entrustment decisions during direct observation encounters of clerkship medical students (3328 unique observations). Supervisors conducted observations of students and collaborated with them to complete an entrustment‐based assessment in which they documented narrative feedback and assigned an entrustment rating. We trained a deep neural network (DNN) to predict entrustment levels from the narrative data and developed an explainable AI protocol to uncover the latent thematic features the DNN used to make its prediction. Results: We found that entrustment levels were associated with level of detail (specific steps for performing clinical tasks), feedback type (constructive versus reinforcing) and task type (procedural versus cognitive). In justifying both high and low levels of entrustment, supervisors detailed concrete steps that trainees performed (or did not yet perform) competently. Conclusions: Framing our results in the factors previously identified as influencing entrustment, we find a focus on performance details related to trainees' clinical competency as opposed to nonspecific feedback on trainee qualities. The entrustment framework reflected in feedback appeared to guide specific goal‐setting, combined with details necessary to reach those goals. Our NLP methodology can also serve as a starting point for future work on entrustment and feedback as similar assessment datasets accumulate.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Providing effective feedback to medical students can be challenging.
“Providing effective feedback to medical students can be challenging [144–147].”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [147]
Cites
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071 · see citing works
Research design: qualitative, quantitative, and mixed methods approaches
Caldas, M. (2003) · Revista de Administração Contemporânea · doi:10.1590/s1415-65552003000100015 · Cited by 18,670 · see citing works
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv · doi:10.48550/arxiv.1603.04467 · Cited by 9,408 · see citing works
Shute, V. J. (2008) · Review of Educational Research · doi:10.3102/0034654307313795 · Cited by 4,631 · see citing works
Bayesian Reasoning and Machine Learning
Barber, D. (2012) · Cambridge University Press eBooks · doi:10.1017/cbo9780511804779 · Cited by 1,663 · see citing works
Validity: on the meaningful interpretation of assessment data
Downing, S. M. (2003) · Medical Education · doi:10.1046/j.1365-2923.2003.01594.x · Cited by 1,434 · see citing works
Conditions Under Which Assessment Supports Students’ Learning
Gibbs, G., & Simpson, C. (2005) · Research Repository (University of Gloucestershire) · Cited by 1,265 · see citing works
Winstone, N. E., Nash, R. A., Parker, M., & Rowntree, J. (2017) · Educational Psychologist · doi:10.1080/00461520.2016.1207538 · Cited by 884 · cited 1× · see citing works
“48 In addition, our results support Robinson et al.'s recent finding from qualitative interviews that the amount of guidance a supervisor provided to a trainee was the primary factor that influenced their entrustment decision, where an increasing need for guidance led to lower ES levels.”
ResultStudents’ perceptions about evaluation and assessment in higher education: a review
Struyven, K., Dochy, F., & Janssens, S. (2005) · Assessment & Evaluation in Higher Education · doi:10.1080/02602930500099102 · Cited by 782 · see citing works
Deep Learning—A Technology With the Potential to Transform Health Care
Hinton, G. (2018) · JAMA · doi:10.1001/jama.2018.11100 · Cited by 667 · see citing works
The “educational alliance” as a framework for reconceptualizing feedback in medical education.
Telio, S., Ajjawi, R., & Regehr, G. (2015) · Academic Medicine · doi:10.1097/acm.0000000000000560 · Cited by 572 · see citing works
Student perceptions of quality feedback in teacher education
Ferguson, P. D. (2009) · Assessment & Evaluation in Higher Education · doi:10.1080/02602930903197883 · Cited by 429 · see citing works
Eva, K. W., Armson, H., Holmboe, E., Lockyer, J., Loney, E., Mann, K., & Sargeant, J. (2012) · Advances in Health Sciences Education · doi:10.1007/s10459-011-9290-7 · Cited by 402 · see citing works
Jelodar, H., Wang, Y., Orji, R., & Huang, S. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3001216 · Cited by 400 · see citing works
Youngstrom, E. A. (2013) · Journal of Pediatric Psychology · doi:10.1093/jpepsy/jst062 · Cited by 393 · see citing works
Wang, X., Peng, Y., Lü, L., Lu, Z., Bagheri, M. H., & Summers, R. M. (2019) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-030-13969-8_18 · Cited by 377 · see citing works
A comparison of word embeddings for the biomedical natural language processing
Wang, Y., Liu, S., Afzal, N., Rastegar-Mojarad, M., Wang, L., Shen, F., Kingsbury, P., & Liu, H. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.09.008 · Cited by 370 · see citing works
Machine learning and medical education
Kolachalama, V. B., & Garg, P. S. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0061-1 · Cited by 320 · see citing works
When Do Supervising Physicians Decide to Entrust Residents With Unsupervised Tasks?
Sterkenburg, A., Barach, P. R., Kalkman, C. J., Gielen, M. J. M., & ten Cate, O. (2010) · Academic Medicine · doi:10.1097/acm.0b013e3181eab0ec · Cited by 276 · see citing works
Feedback on feedback practice: perceptions of students and academics
Mulliner, E., & Tucker, M. (2017) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2015.1103365 · Cited by 261 · see citing works
A call for deep-learning healthcare
Norgeot, B., Glicksberg, B. S., & Butte, A. J. (2018) · Nature Medicine · doi:10.1038/s41591-018-0320-3 · Cited by 252 · see citing works
Feedback and the educational alliance: Examining credibility judgements and their consequences.
Telio, S., Regehr, G., & Ajjawi, R. (2016) · Medical Education · doi:10.1111/medu.13063 · Cited by 231 · see citing works
Trust, competence, and the supervisor's role in postgraduate training
ten Cate, O. (2006) · BMJ · doi:10.1136/bmj.38938.407569.94 · Cited by 218 · see citing works
ten Cate, O., Schwartz, A., & Chen, H. C. (2020) · Academic Medicine · doi:10.1097/acm.0000000000003427 · Cited by 186 · see citing works
The ingredients of a rich entrustment decision
ten Cate, O., & Chen, H. C. (2020) · Medical Teacher · doi:10.1080/0142159x.2020.1817348 · Cited by 150 · see citing works
de Kleijn, R. A. M. (2021) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2021.1967283 · Cited by 141 · see citing works
Deep learning for sentiment analysis
Rojas-Barahona, L. M. (2016) · Language and Linguistics Compass · doi:10.1111/lnc3.12228 · Cited by 131 · see citing works
Ginsburg, S., van der Vleuten, C. P. M., & Eva, K. W. (2017) · Academic Medicine · doi:10.1097/acm.0000000000001669 · Cited by 125 · see citing works
How clinical supervisors develop trust in their trainees: a qualitative study
Hauer, K., Oza, S. K., Kogan, J. R., Stankiewicz, C., Stenfors-Hayes, T., ten Cate, O. T., Batt, J., & O'Sullivan, P. (2015) · Medical Education · doi:10.1111/medu.12745 · Cited by 124 · see citing works
Self-citationReddy, S. T., Zegarek, M. H., Fromme, H. B., Ryan, M. S., Schumann, S.-A., & Harris, I. B. (2015) · Journal of Graduate Medical Education · doi:10.4300/jgme-d-14-00461.1 · Cited by 120 · see citing works
A Review of Natural Language Processing in Medical Education
Chary, M. A., Parikh, S., Manini, A. F., Boyer, E. W., & Radeous, M. (2018) · Western Journal of Emergency Medicine · doi:10.5811/westjem.2018.11.39725 · Cited by 112 · see citing works
The impact of entrustment assessments on feedback and learning: Trainee perspectives
Martin, L. J., Sibbald, M., Vegas, D. B., Russell, D., & Govaerts, M. (2019) · Medical Education · doi:10.1111/medu.14047 · Cited by 87 · see citing works
Pintea, S., & Moldovan, R. (2009) · Journal of Evidence-Based Psychotherapies · Cited by 86 · see citing works
Interpretable clinical prediction via attention-based neural network
Chen, P., Dong, W., Wang, J.-L., Lü, X., Kaymak, U., & Huang, Z. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-1110-7 · Cited by 86 · see citing works
Hauer, K. E., Soni, K., Cornett, P., Kohlwes, J., Hollander, H., Ranji, S. R., ten Cate, O., Widera, E., Calton, B., & O’Sullivan, P. S. (2013) · Journal of General Internal Medicine · doi:10.1007/s11606-013-2372-x · Cited by 83 · see citing works
Self-citationWu, E. H., Elnicki, D. M., Alper, E., Bost, J. E., Corbett, E. C., Fagan, M. J., Mechaber, A. J., Ogden, P. E., Sebastian, J. L., & Torre, D. M. (2008) · Academic Medicine · doi:10.1097/acm.0b013e318183c5a7 · Cited by 79 · see citing works
Holzhausen, Y., Maaz, A., Cianciolo, A. T., ten Cate, O., & Peters, H. (2017) · Perspectives on Medical Education · doi:10.1007/s40037-017-0336-2 · Cited by 78 · see citing works
Deep Learning-Based Approaches for Sentiment Analysis
Agarwal, B., Nayak, R., Mittal, N., & Patnaik, S. (2020) · Algorithms for intelligent systems · doi:10.1007/978-981-15-1216-2 · Cited by 70 · see citing works
Chan, T. M., Sebok‐Syer, S. S., Sampson, C. S., & Monteiro, S. (2020) · Teaching and Learning in Medicine · doi:10.1080/10401334.2019.1708365 · Cited by 65 · see citing works
Tolsgaard, M. G., Boscardin, C., Park, Y. S., Cuddy, M. M., & Sebok‐Syer, S. S. (2020) · Advances in Health Sciences Education · doi:10.1007/s10459-020-10009-8 · Cited by 64 · see citing works
Self-citationShaffer, D. W., & Ruis, A. R. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-67788-6_5 · Cited by 62 · see citing works
Wijnen‐Meijer, M., van der Schaaf, M. F., Booij, E., Harendza, S., Boscardin, C., van Wijngaarden, J., & ten Cate, O. (2013) · Advances in Health Sciences Education · doi:10.1007/s10459-013-9444-x · Cited by 51 · see citing works
Self-citationÖtleş, E., Kendrick, D. E., Solano, Q. P., Schuller, M. C., Ahle, S. L., Eskender, M. H., Carnes, E., & George, B. C. (2021) · Academic Medicine · doi:10.1097/acm.0000000000004153 · Cited by 48 · see citing works
Tensions in Assessment: The Realities of Entrustment in Internal Medicine
Melvin, L., Rassos, J., Stroud, L. F., & Ginsburg, S. (2019) · Academic Medicine · doi:10.1097/acm.0000000000002991 · Cited by 41 · see citing works
Cutrer, W. B., Russell, R. G., Davidson, M., & Lomis, K. D. (2019) · Medical Teacher · doi:10.1080/0142159x.2019.1686135 · Cited by 34 · see citing works
How Residents Develop Trust in Interns: A Multi-Institutional Mixed-Methods Study
Sheu, L. C., O’Sullivan, P., Aagaard, E., Tad‐y, D., Harrell, H. E., Kogan, J. R., Nixon, L. J., Hollander, H., & Hauer, K. E. (2016) · Academic Medicine · doi:10.1097/acm.0000000000001164 · Cited by 30 · see citing works
Self-citationFaculty and Resident Perspectives on Using Entrustment Anchors for Workplace-Based Assessment
Dudek, N. L., Gofton, W. T., Rekman, J. F., & McDougall, A. (2019) · Journal of Graduate Medical Education · doi:10.4300/jgme-d-18-01003.1 · Cited by 30 · see citing works
Stahl, C. C., Jung, S. A., Rosser, A. A., Kraut, A., Schnapp, B. H., Westergaard, M. C., Hamedani, A. G., Minter, R. M., & Greenberg, J. A. (2020) · The American Journal of Surgery · doi:10.1016/j.amjsurg.2020.11.044 · Cited by 29 · see citing works
Entrustment Unpacked: Aligning Purposes, Stakes, and Processes to Enhance Learner Assessment
Kinnear, B., Warm, E. J., Caretta‐Weyer, H. A., Holmboe, E. S., Turner, D. A., van der Vleuten, C. P. M., & Schumacher, D. J. (2021) · Academic Medicine · doi:10.1097/acm.0000000000004108 · Cited by 29 · see citing works
A Comparison of Word Embeddings for the Biomedical Natural Language ProcessingPreprint
Wang, Y., Liu, S., Afzal, N., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.00400 · Cited by 28 · see citing works
How Cognitive Machines Can Augment Medical Imaging
Miller, D. D., & Brown, E. W. (2018) · American Journal of Roentgenology · doi:10.2214/ajr.18.19914 · Cited by 28 · see citing works
Bonnie, L. H. A., Visser, M. R. M., Kramer, A. W. M., & van Dijk, N. (2020) · BMJ Open · doi:10.1136/bmjopen-2019-036593 · Cited by 28 · see citing works
Salt, J., Harik, P., & Barone, M. (2018) · Academic Medicine · doi:10.1097/acm.0000000000002558 · Cited by 23 · see citing works
Interpretable Neural Architectures for Attributing an Ad’s Performance to its Writing Style
Pryzant, R., Basu, S., & Sone, K. (2018) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/w18-5415 · Cited by 22 · see citing works
Automated assessment of medical training evaluation text.
Zhang, R., Pakhomov, S., Gladding, S. P., Aylward, M., Borman-Shoap, E. C., & Melton, G. B. (2012) · PubMed Central · Cited by 17 · see citing works
Exploring the use of rating scales with entrustment anchors in workplace‐based assessment
Robinson, T. J. G., Wagner, N., Szulewski, A., Dudek, N. L., Cheung, W. J., & Hall, A. K. (2021) · Medical Education · doi:10.1111/medu.14573 · Cited by 16 · see citing works
Foote, D. C., Reddy, R. M., Matusko, N., & Sandhu, G. (2020) · The American Journal of Surgery · doi:10.1016/j.amjsurg.2020.02.052 · Cited by 11 · see citing works
Gilchrist, T., Hatala, R. A., & Gingerich, A. (2021) · Perspectives on Medical Education · doi:10.1007/s40037-021-00652-1 · Cited by 11 · see citing works
ten Cate, O. (2016) · Medical Science Educator · doi:10.1007/s40670-016-0342-8 · Cited by 10 · see citing works
LSTMs Compose—and Learn—Bottom-Up
Saphra, N., & Lopez, A. (2020) · doi:10.18653/v1/2020.findings-emnlp.252 · Cited by 10 · see citing works
Dolan, B. M., O’Brien, C. L., & Green, M. M. (2017) · Medical Science Educator · doi:10.1007/s40670-017-0434-0 · Cited by 9 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Ethical use of Artificial Intelligence in Health Professions Education: AMEE Guide No. 158
Masters, K. (2023) · Medical Teacher · doi:10.1080/0142159x.2023.2186203 · Cited by 302 · see citing works
The fundamentals of Artificial Intelligence in medical education research: AMEE Guide No. 156.
Tolsgaard, M. G., Pusic, M. V., Sebok-Syer, S. S., Gin, B., Svendsen, M. B., Syer, M. D., Brydges, R., Cuddy, M. M., & Boscardin, C. K. (2023) · Medical Teacher · doi:10.1080/0142159x.2023.2180340 · Cited by 137 · see citing works
Self-citationCook, D. A., Overgaard, J., Pankratz, V. S., Del Fiol, G., & Aakre, C. A. (2025) · Journal of Medical Internet Research · doi:10.2196/68486 · Cited by 96 · see citing works
Huang, G., Li, Y., Jameel, S., Long, Y., & Papanastasiou, G. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.05.004 · Cited by 74 · see citing works
The Review of Studies on Explainable Artificial Intelligence in Educational Research
Türkmen, G. (2024) · Journal of Educational Computing Research · doi:10.1177/07356331241310915 · Cited by 79 · see citing works
Artificial intelligence in undergraduate medical education: an updated scoping review
Simoni, J., Urtubia-Fernandez, J., Mengual, E., Simoni, D. A., Royo, M., Egaña-Yin, D., Hertog, O. L. A., López-Ortiz, L., Muñoz-Tomás, A., Santiago-Martínez, P., et al. (2025) · BMC Medical Education · doi:10.1186/s12909-025-08188-2 · Cited by 62 · see citing works
Applications of Artificial Intelligence in Medical Education: A Systematic Review
Hallquist, E., Gupta, I., Montalbano, M. J., & Loukas, M. (2025) · Cureus · doi:10.7759/cureus.79878 · Cited by 47 · see citing works
Towards Trustworthy and Explainable AI Educational Systems
Shafik, W. (2024) · Information systems engineering and management · doi:10.1007/978-3-031-72410-7_2 · Cited by 45 · see citing works
Gin, B. C., O’Sullivan, P., Hauer, K. E., Abdulnour, R.-E. E., Mackenzie, M. E., ten Cate, O., & Boscardin, C. (2024) · Academic Medicine · doi:10.1097/acm.0000000000005930 · Cited by 42 · see citing works
Self-citationFernandes, R. D., de Vries, I., McEwen, L. A., Mann, S. M., Phillips, T., & Zevin, B. (2024) · Annals of Surgery · doi:10.1097/sla.0000000000006308 · Cited by 31 · see citing works
Maimone, C., Dolan, B. M., Green, M. M., Sanguino, S. M., Garcia, P. M., & O’Brien, C. L. (2023) · Perspectives on Medical Education · doi:10.5334/pme.40 · Cited by 17 · see citing works
Gin, B. C., ten Cate, O., O’Sullivan, P. S., & Boscardin, C. (2024) · Advances in Health Sciences Education · doi:10.1007/s10459-024-10311-9 · Cited by 14 · see citing works
Self-citationLi, C., Tsuei, S. H., & Wu, H. (2025) · JMIR Medical Education · doi:10.2196/80604 · Cited by 12 · see citing works
Role of AI in Competency-Based Medical Education: Using EPA as the Magicbox
Dabbagh, A., Madadi, F., & Larijani, B. A. (2024) · Archives of Iranian Medicine · doi:10.34172/aim.31795 · Cited by 9 · see citing works
Maimone, C., Dolan, B. M., Green, M. M., Sanguino, S. M., & O’Brien, C. L. (2024) · Academic Medicine · doi:10.1097/acm.0000000000005800 · Cited by 7 · see citing works
Evolving natural language processing towards a subjectivist inductive paradigm
Gin, B. C. (2023) · Medical Education · doi:10.1111/medu.15024 · Cited by 6 · see citing works
Self-citationWhy Did the AI Suggest That? Designing an Explainable Educational Counseling System
Fan, Z., Chen, P., & Lu, Y. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-98459-4_23 · Cited by 5 · see citing works
Wang, H., Shan, W., Liu, R. Z., & Wang, Z. (2025) · Digital Health · doi:10.1177/20552076251390280 · Cited by 4 · see citing works
Hui, L., Yu, E., Chung, A. D., & Kwan, B. Y. M. (2025) · Postgraduate Medical Journal · doi:10.1093/postmj/qgaf195 · Cited by 4 · see citing works
Costa-Dookhan, K. A., Adirim, Z. L., Maslej, M. M., Donner, K., Rodak, T., Soklaridis, S., Sockalingam, S., & Thakur, A. (2025) · Academic Medicine · doi:10.1097/acm.0000000000005983 · Cited by 4 · see citing works
AI’s Accuracy in Extracting Learning Experiences From Clinical Practice Logs: Observational Study
Kondo, T., & Nishigori, H. (2025) · JMIR Medical Education · doi:10.2196/68697 · Cited by 4 · see citing works
Kerth, J.-L., & Bosse, H. M. (2024) · Academic Pediatrics · doi:10.1016/j.acap.2024.06.017 · Cited by 3 · see citing works
Assessment of Entrustable Professional Activities Among Dutch Endocrine Supervisors
de Laat, J. M., van der Horst‐Schrivers, A. N. A., Appelman‐Dijkstra, N. M., Bisschop, P. H. L. T., Dreijerink, K. M. A., Drent, M. L., van der Klauw, M. M., de Ranitz‐Greven, W. L., Stades, A. M. E., Stikkelbroeck, N. M. M. L., et al. (2024) · Journal of CME · doi:10.1080/28338073.2024.2360137 · Cited by 1 · see citing works
Cafro, C. B., Tao, L. A., Lindley, A., ten Cate, O., & Kim, E. A. (2026) · Journal of surgical education · doi:10.1016/j.jsurg.2026.104082 · Cited by 1 · see citing works
Tolsgaard, M. G., Grierson, L., Pusic, M. V., & Turner, L. (2026) · Medical Education · doi:10.1111/medu.70309 · Cited by 2 · see citing works
Embracing learning analytics in health professions education
Hibbard, S. T., McClure, J., & Kellogg, S. (2024) · New Directions for Teaching and Learning · doi:10.1002/tl.20597 · Cited by 1 · see citing works
Security Requirements Classification by Means of Explainable Transformer Models
Petrillo, L., Martinelli, F., Santone, A., & Mercaldo, F. (2025) · IEEE European Symposium on Security and Privacy Workshops (EuroS&PW) · doi:10.1109/eurospw67616.2025.00064 · Cited by 1 · see citing works
Gin, B. C., ten Cate, O., O’Sullivan, P., & Boscardin, C. (2023) · Research Square · doi:10.21203/rs.3.rs-3223749/v1 · Cited by 1 · see citing works
Self-citationAssessment of Entrustable Professional Activities among Dutch Endocrine SupervisorsPreprint
de Laat, J. M., van der Horst‐Schrivers, A. N. A., Appelman‐Dijkstra, N. M., Bisschop, P. H. L. T., Dreijerink, K. M. A., Drent, M. L., van der Klauw, M. M., de Ranitz‐Greven, W. L., Stades, A. M. E., Stikkelbroeck, N. M. M. L., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-3256755/v1
Kundu, S. (2024) · International Journal For Multidisciplinary Research · doi:10.36948/ijfmr.2024.v06i03.40668
Li, C., Tsuei, S. H., & Wu, H. (2025) · doi:10.2196/preprints.80604
Santamaria, T. (2025) · Primary Care Clinics in Office Practice · doi:10.1016/j.pop.2025.07.006
Allen, R., de Vries, I., Murray, T. M. M., Ball, N., McGuire, A., Mann, S. M., Mizubuti, G. B., Szász, P., & Zevin, B. (2025) · Journal of surgical education · doi:10.1016/j.jsurg.2025.103803
Al-Sulaiman, A., Heeneman, S., Iqbal, M. Z., & van der Vleuten, C. P. M. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1712735
Boocock, R. C., Jones, C., & Lake, A. A. (2026) · Journal of Human Nutrition and Dietetics · doi:10.1111/jhn.70287
Artificial intelligence in emergency medicine education: A narrative review
Pelletier, J., Arango-Ibañez, J. P., Taylor, R. A., Smith, M. E., Gottlieb, M. A., Jones, D. D., Colletti, J. E., Rego, A., & Long, B. (2026) · The American Journal of Emergency Medicine · doi:10.1016/j.ajem.2026.09.006
Carter, E., Mensah, K., Papadopoulou, S., & Weber, T. (2023) · Annals of Pharmacy Education Safety and Public Health Advocacy · doi:10.51847/7tfsyswaob
- Authors
- Gin, B. C., ten Cate, O., O'Sullivan, P. S., Hauer, K. E., & Boscardin, C.
- Affiliations
- University of California, San Francisco (US)
- Utrecht Center for Research and Development of Health Professions Education University Medical Center Utrecht The Netherlands
- Department of Medicine University of California San Francisco San Francisco CA USA
- Corresponding author
- Gin, Brian C.
- Venue
- Medical Education
- Publisher
- Wiley
- Published
- 2021-12
- Volume
- 56
- Issue
- 3
- Pages
- 303-311
- ISSN
- 0308-0110 (ISSN-L)
- 1365-2923
- OpenAlex
- W3213621237
- Cited by
- 37
- References
- 62
- Citations by year
- 2026: 5 · 2025: 13 · 2024: 12 · 2023: 7
- FWCI
- 4.11
- Type
- journal article
- Language
- en
- Open access
- green
- dspace.library.uu.nl
- Licence
- Locations
- Medical Education (publishedVersion)
- PubMed (publishedVersion)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- eScholarship (California Digital Library) (submittedVersion)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
- Keywords
- entrustment, clinical supervision, narrative feedback, medical students, clinical rotations, clinical competence, feedback types, task type, goal setting, explainable AI, natural language processing, deep neural network inference
- MeSH terms
- Artificial Intelligence, Clinical Competence, Competency-Based Education, Feedback, Humans, Internship and Residency, Learning, Students, Medical
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
