Inteligência Artificial na Graduação Médica: uma revisão de escopo das aplicações educacionais
DOI:
https://doi.org/10.17765/2176-9206.2026v19e13977Palavras-chave:
Educação Médica, Estudante, Ensino, Aprendizagem, Inteligência ArtificialResumo
O objetivo foi mapear a aplicação da inteligência artificial (IA) na graduação médica, com ênfase nas ferramentas utilizadas, desfechos de aprendizagem, desempenho e lacunas da literatura. As buscas foram realizadas no PubMed, Scopus, Web of Science, EBSCOhost (CINAHL/MEDLINE), ScienceDirect e Google Scholar, incluindo estudos empíricos em inglês, espanhol e português, sem restrição temporal. A seleção dos estudos foi realizada em duplicata utilizando o Rayyan, e os achados foram descritos a partir do referencial TPACK. Dos 605 registros, 58 estudos (2018–2024) foram incluídos, majoritariamente de países de alta renda. Predominaram delineamentos quantitativos, com amostras de pequeno a médio porte. A IA foi aplicada principalmente ao ensino teórico, avaliação educacional e treinamento em simulações, frequentemente associada a melhorias no desempenho acadêmico e habilidades objetivas. Conclui-se que a IA demonstra potencial educacional, mas permanece limitada por heterogeneidade metodológica, lacunas teóricas e desigualdades contextuais, ressaltando a necessidade de integração curricular estruturada, ética e longitudinal.
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Referências
1.Dave M, Patel N. Artificial intelligence in healthcare and education. British Dental Journal. 2023;234(10): 761–764. https://doi.org/10.1038/s41415-023-5845-2.
2. Jaworski A, Jasi?ski D, Jaworski W, Hop A, Janek A, S?awi?ska B, et al. Comparison of the Performance of Artificial Intelligence Versus Medical Professionals in the Polish Final Medical Examination. Cureus. 2024;16(8): e66011. https://doi.org/10.7759/cureus.66011.
3. Yousefi F, Naye F, Ouellet S, Yameogo AR, Sasseville M, Bergeron F, et al. Artificial Intelligence in Health Promotion and Disease Reduction: Rapid Review. Journal of Medical Internet Research. 2025;27: e70381–e70381. https://doi.org/10.2196/70381.
4. Alqahtani T, Badreldin HA, Alrashed M, Alshaya AI, Alghamdi SS, bin Saleh K, et al. The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research. Research in Social and Administrative Pharmacy. 2023;19(8): 1236–1242. https://doi.org/10.1016/j.sapharm.2023.05.016.
5. Meyer A, Riese J, Streichert T. Comparison of the Performance of GPT-3.5 and GPT-4 With That of Medical Students on the Written German Medical Licensing Examination: Observational Study. JMIR Medical Education. 2024;10(1). https://doi.org/10.2196/50965.
6. Smith A, Arena R, Bacon SL, Faghy MA, Grazzi G, Raisi A, et al. Recommendations on the use of artificial intelligence in health promotion. Progress in Cardiovascular Diseases. 2024;87: 37–43. https://doi.org/10.1016/j.pcad.2024.10.003.
7. Qian Y, Siau KL. Advances in IoT, AI, and Sensor-Based Technologies for Disease Treatment, Health Promotion, Successful Ageing, and Ageing Well. Sensors. 2025;25(19): 6207. https://doi.org/10.3390/s25196207.
8. Krive J, Isola M, Chang L, Patel T, Anderson M, Sreedhar R. Grounded in reality: artificial intelligence in medical education. JAMIA Open. 2023;6(2). https://doi.org/10.1093/jamiaopen/ooad037.
9. Park SH, Pinto-Powell R, Thesen T, Lindqwister A, Levy J, Chacko R, et al. Preparing healthcare leaders of the digital age with an integrative artificial intelligence curriculum: a pilot study. Medical Education Online. 2024;29(1). https://doi.org/10.1080/10872981.2024.2315684.
10. Birks S, Gray J, Darling-Pomranz C. Using artificial intelligence to provide a ‘flipped assessment’ approach to medical education learning opportunities. Medical Teacher. 2024;47(8): 1377. https://doi.org/10.1080/0142159X.2024.2434101.
11. Thomae A V., Witt CM, Barth J. Integration of ChatGPT Into a Course for Medical Students: Explorative Study on Teaching Scenarios, Students’ Perception, and Applications. JMIR Medical Education. 2024;10. https://doi.org/10.2196/50545.
12. Driesnack S, Rücker F, Dietze-Jergus N, Bondarenko A, Pletz MW, Viehweger A. A practice-based approach to teaching antimicrobial therapy using artificial intelligence and gamified learning. JAC-Antimicrobial Resistance. 2024;6(4). https://doi.org/10.1093/jacamr/dlae099.
13. Soulage CO, Van Coppenolle F, GUEBRE-EGZIABHER F. The conversational AI ‘ChatGPT’ outperforms medical students on a physiology university examination. Advances in Physiology Education. 2024;48(4): 677–684. https://doi.org/10.1152/advan.00181.2023.
14. Zare S, Vafaeian S, Amini M, Farhadi K, Vali M, Golestani A. Comparing the performance of ChatGPT-3.5-Turbo, ChatGPT-4, and Google Bard with Iranian students in pre-internship comprehensive exams. Scientific Reports. 2024;14(1): 28456. https://doi.org/10.1038/s41598-024-79335-w.
15. Boscardin CK, Gin B, Golde PB, Hauer KE. ChatGPT and Generative Artificial Intelligence for Medical Education: Potential Impact and Opportunity. Academic Medicine. 2024;99(1): 22–27. https://doi.org/10.1097/ACM.0000000000005439.
16. Sridharan K, Sequeira RP. Artificial intelligence and medical education: application in classroom instruction and student assessment using a pharmacology & therapeutics case study. BMC Medical Education. 2024;24(1):431.https://doi.org/10.1186/s12909-024-05365-7.
17. Friederichs H, Friederichs WJ, März M. ChatGPT in medical school: how successful is AI in progress testing? Medical Education Online. 2023;28(1). https://doi.org/10.1080/10872981.2023.2220920.
18. Huh S. Are ChatGPT’s knowledge and interpretation ability comparable to those of medical students in Korea for taking a parasitology examination?: a descriptive study. Journal of Educational Evaluation for Health Professions. 2023;20: 1. https://doi.org/10.3352/jeehp.2023.20.1.
19. Collins BR, Black EW, Rarey KE. Introducing AnatomyGPT: A customized artificial intelligence application for anatomical sciences education. Clinical Anatomy. 2024;37(6): 661–669. https://doi.org/10.1002/ca.24178.
20. Borchert RJ, Hickman CR, Pepys J, Sadler TJ. Performance of ChatGPT on the Situational Judgement Test—A Professional Dilemmas–Based Examination for Doctors in the United Kingdom. JMIR Medical Education. 2023;9. https://doi.org/10.2196/48978.
21. Al-Worafi YM, Goh KW, Hermansyah A, Tan CS, Ming LC. The Use of ChatGPT for Education Modules on Integrated Pharmacotherapy of Infectious Disease: Educators’ Perspectives. JMIR Medical Education. 2024;10(1). https://doi.org/10.2196/47339.
22. Mishra P, Koehler MJ. Technological Pedagogical Content Knowledge: A Framework for Teacher Knowledge. Teachers College Record: The Voice of Scholarship in Education. 2006;108(6): 1017–1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x.
23. Abbas A, Rehman MS, Rehman SS. Comparing the Performance of Popular Large Language Models on the National Board of Medical Examiners Sample Questions. Cureus. 2024;16(3). https://doi.org/10.7759/cureus.55991.
24. Abid A, Murugan A, Banerjee I, Purkayastha S, Trivedi H, Gichoya J. AI Education for Fourth-Year Medical Students: Two-Year Experience of a Web-Based, Self-Guided Curriculum and Mixed Methods Study. JMIR Medical Education. 2024;10(1). https://doi.org/10.2196/46500.
25. Burke HB, Hoang A, Lopreiato JO, King H, Hemmer P, Montgomery M, et al. Assessing the Ability of a Large Language Model to Score Free-Text Medical Student Clinical Notes: Quantitative Study. JMIR Medical Education. 2024;10. https://doi.org/10.2196/56342.
26. Cianciolo AT, Lavoie N, Parker J. Machine Scoring of Medical Students’ Written Clinical Reasoning: Initial Validity Evidence. Academic Medicine. 2021;96(7): 1026–1035. https://doi.org/10.1097/ACM.0000000000004010.
27. Gin BC, ten Cate O, O’Sullivan PS, Hauer KE, Boscardin C. Exploring how feedback reflects entrustment decisions using artificial intelligence. Medical Education. 2022;56(3): 303–311. https://doi.org/10.1111/medu.14696.
28. Ledwos N, Mirchi N, Yilmaz R, Winkler-Schwartz A, Sawni A, Fazlollahi AM, et al. Assessment of learning curves on a simulated neurosurgical task using metrics selected by artificial intelligence. Journal of Neurosurgery. 2022;137(4): 1160–1171. https://doi.org/10.3171/2021.12.JNS211563.
29. Mirchi N, Bissonnette V, Yilmaz R, Ledwos N, Winkler-Schwartz A, Del Maestro RF. The virtual operative assistant: An explainable artificial intelligence tool for simulation-based training in surgery and medicine. PLoS ONE. 2020;15(2). https://doi.org/10.1371/journal.pone.0229596.
30. Morjaria L, Burns L, Bracken K, Ngo QN, Lee M, Levinson AJ, et al. Examining the Threat of ChatGPT to the Validity of Short Answer Assessments in an Undergraduate Medical Program. Journal of Medical Education and Curricular Development. 2023;10. https://doi.org/10.1177/23821205231204178.
31. Nagaraj MB, Namazi B, Sankaranarayanan G, Scott DJ. Developing artificial intelligence models for medical student suturing and knot-tying video-based assessment and coaching. Surgical Endoscopy. 2023;37(1): 402–411. https://doi.org/10.1007/s00464-022-09509-y.
32. Ruberto AJ, Rodenburg D, Ross K, Sarkar P, Hungler PC, Etemad A, et al. The future of simulation-based medical education: Adaptive simulation utilizing a deep multitask neural network. AEM Education and Training. 2021;5(3). https://doi.org/10.1002/aet2.10605.
33. Scherr R, Halaseh FF, Spina A, Andalib S, Rivera R. ChatGPT Interactive Medical Simulations for Early Clinical Education: Case Study. JMIR Medical Education. 2023;9(1). https://doi.org/10.2196/49877.
34. Yilmaz R, Fazlollahi AM, Winkler-Schwartz A, Wang A, Makhani HH, Alsayegh A, et al. Effect of Feedback Modality on Simulated Surgical Skills Learning Using Automated Educational SystemsÀ A Four-Arm Randomized Control Trial. Journal of Surgical Education. 2023;81(2): 275–287. https://doi.org/10.6084/m9.figshare.15132507.v1.
35. Yilmaz R, Fazlollahi AM, Winkler-Schwartz A, Wang A, Makhani HH, Alsayegh A, et al. Effect of Feedback Modality on Simulated Surgical Skills Learning Using Automated Educational Systems– A Four-Arm Randomized Control Trial. Journal of Surgical Education. 2024;81(2): 275–287. https://doi.org/https://doi.org/10.1016/j.jsurg.2023.11.001.
36. Fazlollahi AM, Bakhaidar M, Alsayegh A, Yilmaz R, Winkler-Schwartz A, Mirchi N, et al. Effect of artificial intelligence tutoring vs expert instruction on learning simulated surgical skills among medical students a randomized clinical trial. JAMA Network Open. 2022;5(2). https://doi.org/10.1001/jamanetworkopen.2021.49008.
37. Fazlollahi AM, Yilmaz R, Winkler-Schwartz A, Mirchi N, Ledwos N, Bakhaidar M, et al. AI in Surgical Curriculum Design and Unintended Outcomes for Technical Competencies in Simulation Training. JAMA network open. 2023;6(9): e2334658. https://doi.org/10.1001/jamanetworkopen.2023.34658.
38. Baj?eti? M, Mir?i? A, Rako?evi? J, ?okovi? D, Milutinovi? K, Zaletel I. Comparing the performance of artificial intelligence learning models to medical students in solving histology and embryology multiple choice questions. Annals of Anatomy - Anatomischer Anzeiger. 2024;254: 152261. https://doi.org/https://doi.org/10.1016/j.aanat.2024.152261.
39. Co?kun Ö, K?yak YS, Budako?lu I?. ChatGPT to generate clinical vignettes for teaching and multiple-choice questions for assessment: A randomized controlled experiment. Medical Teacher. 2024;(2): 268–274. https://doi.org/10.1080/0142159X.2024.2327477.
40. Holderried F, Stegemann-Philipps C, Herrmann-Werner A, Festl-Wietek T, Holderried M, Eickhoff C, et al. A Language Model–Powered Simulated Patient With Automated Feedback for History Taking: Prospective Study. JMIR Medical Education. 2024;10. https://doi.org/10.2196/59213.
41. Lippitsch A, Steglich J, Ludwig C, Kellner J, Hempel L, Stoevesandt D, et al. Development and evaluation of a software system for medical students to teach and practice anamnestic interviews with virtual patient avatars. Computer Methods and Programs in Biomedicine. 2024;244: 107964. https://doi.org/https://doi.org/10.1016/j.cmpb.2023.107964.
42. Moglia A, Morelli L, D’Ischia R, Fatucchi LM, Pucci V, Berchiolli R, et al. Ensemble deep learning for the prediction of proficiency at a virtual simulator for robot-assisted surgery. Surgical Endoscopy. 2022;36(9): 6473–6479. https://doi.org/10.1007/s00464-021-08999-6.
43. Riedel M, Kaefinger K, Stuehrenberg A, Ritter V, Amann N, Graf A, et al. ChatGPT’s performance in German OB/GYN exams – paving the way for AI-enhanced medical education and clinical practice. Frontiers in Medicine. 2023;10. https://doi.org/10.3389/fmed.2023.1296615.
44. Tomova M, Roselló Atanet I, Sehy V, Sieg M, März M, Mäder P. Leveraging large language models to construct feedback from medical multiple-choice Questions. Scientific Reports. 2024;14(1). https://doi.org/10.1038/s41598-024-79245-x.
45. Barui S, Sanyal P, Rajmohan KS, Malik A, Dudani S. Perception without preconception: comparison between the human and machine learner in recognition of tissues from histological sections. Scientific Reports. 2022;12(1). https://doi.org/10.1038/s41598-022-20012-1.
46. Cheng CT, Chen CC, Fu CY, Chaou CH, Wu YT, Hsu CP, et al. Artificial intelligence-based education assists medical students’ interpretation of hip fracture. Insights into Imaging. 2020;11(1). https://doi.org/10.1186/s13244-020-00932-0.
47. Ghosh A, Maini Jindal N, Gupta VK, Bansal E, Kaur Bajwa N, Sett A. Is ChatGPT’s Knowledge and Interpretative Ability Comparable to First Professional MBBS (Bachelor of Medicine, Bachelor of Surgery) Students of India in Taking a Medical Biochemistry Examination? Cureus. 2023;15(10). https://doi.org/10.7759/cureus.47329.
48. Huang CH, Hsiao HJ, Yeh PC, Wu KC, Kao CH. Performance of ChatGPT on Stage 1 of the Taiwanese medical licensing exam. Digital Health. 2024;10. https://doi.org/10.1177/20552076241233144.
49. Kinoshita K, Maruyama T, Kobayashi N, Imanishi S, Maruyama M, Ohira G, et al. An artificial intelligence-based nerve recognition model is useful as surgical support technology and as an educational tool in laparoscopic and robot-assisted rectal cancer surgery. Surgical Endoscopy. 2024;38(9): 5394–5404. https://doi.org/10.1007/s00464-024-10939-z.
50. Li J, Ouyang J, Liu J, Zhang F, Wang Z, Guo X, et al. Artificial Intelligence-based online platform assists blood cell morphology learning: A mixed-methods sequential explanatory designed research. Medical Teacher. 2023;45(6): 596–603. https://doi.org/10.1080/0142159X.2023.2190483.
51. Su JM, Hsu SY, Fang TY, Wang PC. Developing and validating a knowledge-based AI assessment system for learning clinical core medical knowledge in otolaryngology. Computers in Biology and Medicine. 2024;178. https://doi.org/10.1016/j.compbiomed.2024.108765.
52. Zheng K, Shen Z, Chen Z, Che C, Zhu H. Application of AI-empowered scenario-based simulation teaching mode in cardiovascular disease education. BMC Medical Education. 2024;24(1). https://doi.org/10.1186/s12909-024-05977-z.
53. Alessi MR, Gomes HA, Lopes de Castro M, Terumy Okamoto C. Performance of ChatGPT in Solving Questions From the Progress Test (Brazilian National Medical Exam): A Potential Artificial Intelligence Tool in Medical Practice. Cureus. 2024;16(7). https://doi.org/10.7759/cureus.64924.
54. Alonso-Silverio GA, Pérez-Escamirosa F, Bruno-Sanchez R, Ortiz-Simon JL, Muñoz-Guerrero R, Minor-Martinez A, et al. Development of a Laparoscopic Box Trainer Based on Open Source Hardware and Artificial Intelligence for Objective Assessment of Surgical Psychomotor Skills. Surgical Innovation. 2018;25(4): 380–388. https://doi.org/10.1177/1553350618777045.
55. Castellano MS, Contreras-McKay I, Neyem A, Farfán E, Inzunza O, Ottone NE, et al. Empowering human anatomy education through gamification and artificial intelligence: An innovative approach to knowledge appropriation. Clinical Anatomy. 2024;37(1): 12–24. https://doi.org/10.1002/ca.24074.
56. Aronovitz N, Hazan I, Jedwab R, Shitrit I Ben, Quinn A, Wacht O, et al. The effect of real-time EF automatic tool on cardiac ultrasound performance among medical students. PLoS ONE. 2024;19(3). https://doi.org/10.1371/journal.pone.0299461.
57. Meo SA, AbuKhalaf AA, Meo MZS, Meo MOS, Ayub R, ElToukhy RA, et al. Role of artificial intelligence (Google bard) in morphological, histopathological, and radiological image identifications: Objective Structured Practical Examination (OSPE) type-based performance. Saudi medical journal. 2024;45(5): 531–536. https://doi.org/10.15537/smj.2024.45.5.20240141.
58. Mavrych V, Ganguly P, Bolgova O. Using large language models (ChatGPT, Copilot, PaLM, Bard, and Gemini) in Gross Anatomy course: Comparative analysis. Clinical Anatomy. 2024;38(2): 200–210. https://doi.org/10.1002/ca.24244.
59. Cherif H, Moussa C, Missaoui AM, Salouage I, Mokaddem S, Dhahri B. Appraisal of ChatGPT’s Aptitude for Medical Education: Comparative Analysis With Third-Year Medical Students in a Pulmonology Examination. JMIR Medical Education. 2024;10: e52818. https://doi.org/10.2196/52818.
60. Squalli Houssaini M, Aboutajeddine A, Toughrai I, Ibrahimi A. Development of a design course for medical curriculum: Using design thinking as an instructional design method empowered by constructive alignment and generative AI. Thinking Skills and Creativity. 2024;52: 101491. https://doi.org/https://doi.org/10.1016/j.tsc.2024.101491.
61. Xie Y, Seth I, Hunter-Smith DJ, Rozen WM, Seifman MA. Investigating the impact of innovative AI chatbot on post-pandemic medical education and clinical assistance: a comprehensive analysis. ANZ Journal of Surgery. 2024;94(1–2): 68–77. https://doi.org/10.1111/ans.18666.
62. Gan W, Ouyang J, Li H, Xue Z, Zhang Y, Dong Q, et al. Integrating ChatGPT in Orthopedic Education for Medical Undergraduates: Randomized Controlled Trial. Journal of Medical Internet Research. 2024;26. https://doi.org/10.2196/57037.
63. Baglivo F, De Angelis L, Casigliani V, Arzilli G, Privitera GP, Rizzo C. Exploring the Possible Use of AI Chatbots in Public Health Education: Feasibility Study. JMIR Medical Education. 2023;9. https://doi.org/10.2196/51421.
64. Tsopra R, Peiffer-Smadja N, Charlier C, Campeotto F, Lemogne C, Ruszniewski P, et al. Putting undergraduate medical students in AI-CDSS designers’ shoes: An innovative teaching method to develop digital health critical thinking. International Journal of Medical Informatics. 2023;171: 104980. https://doi.org/https://doi.org/10.1016/j.ijmedinf.2022.104980.
65. Bharatha A, Ojeh N, Rabbi AMF, Campbell MH, Krishnamurthy K, Layne-Yarde RNA, et al. Comparing the Performance of ChatGPT-4 and Medical Students on MCQs at Varied Levels of Bloom’s Taxonomy. Advances in Medical Education and Practice. 2024;15: 393–400. https://doi.org/10.2147/AMEP.S457408.
66. Furlan R, Gatti M, Menè R, Shiffer D, Marchiori C, Giaj Levra A, et al. A Natural Language Processing–Based Virtual Patient Simulator and Intelligent Tutoring System for the Clinical Diagnostic Process: Simulator Development and Case Study. JMIR Medical Informatics. 2021;9(4). https://doi.org/https://doi.org/10.2196/24073.
67. Fazlollahi AM, Bakhaidar M, Alsayegh A, Yilmaz R, Winkler-Schwartz A, Mirchi N, et al. Effect of Artificial Intelligence Tutoring vs Expert Instruction on Learning Simulated Surgical Skills Among Medical Students: A Randomized Clinical Trial. JAMA Network Open. 2022;5(2). https://doi.org/10.1001/jamanetworkopen.2021.49008.
68. AlZaabi A, AlMaskari S, AalAbdulsalam A. Are physicians and medical students ready for artificial intelligence applications in healthcare? Digital Health. 2023;9. https://doi.org/10.1177/20552076231152167.
69. Buabbas AJ, Miskin B, Alnaqi AA, Ayed AK, Shehab AA, Syed-Abdul S, et al. Investigating Students’ Perceptions towards Artificial Intelligence in Medical Education. Healthcare. 2023;11(9): 1298. https://doi.org/10.3390/healthcare11091298.
70. Ejaz H, McGrath H, Wong BL, Guise A, Vercauteren T, Shapey J. Artificial intelligence and medical education: A global mixed-methods study of medical students’ perspectives. DIGITAL HEALTH. 2022;8: 205520762210890. https://doi.org/10.1177/20552076221089099.
71. Kimmerle J, Timm J, Festl-Wietek T, Cress U, Herrmann-Werner A. Medical Students’ Attitudes Toward AI in Medicine and their Expectations for Medical Education. Journal of Medical Education and Curricular Development. 2023;10. https://doi.org/10.1177/23821205231219346.
72. Al Saad MM, Shehadeh A, Alanazi S, Alenezi M, Abu alez A, Eid H, et al. Medical Students’ Knowledge and Attitude Towards Artificial Intelligence: An Online Survey. The Open Public Health Journal. 2022;15. https://doi.org/https://doi.org/10.2174/18749445-v15-e2203290.
73. Lin S. A Clinician’s Guide to Artificial Intelligence (AI): Why and How Primary Care Should Lead the Health Care AI Revolution. The Journal of the American Board of Family Medicine. 2022;35(1): 175–184. https://doi.org/10.3122/jabfm.2022.01.210226.
74. Bera K, Schalper KA, Rimm DL, Velcheti V, Madabhushi A. Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology. Nature Reviews Clinical Oncology. 2019;16(11): 703–715. https://doi.org/10.1038/s41571-019-0252-y.
75. Dhombres F, Bonnard J, Bailly K, Maurice P, Papageorghiou AT, Jouannic JM. Contributions of Artificial Intelligence Reported in Obstetrics and Gynecology Journals: Systematic Review. Journal of Medical Internet Research. 2022;24(4): e35465. https://doi.org/10.2196/35465.
76. Rösler W, Altenbuchinger M, Baeßler B, Beissbarth T, Beutel G, Bock R, et al. An overview and a roadmap for artificial intelligence in hematology and oncology. Journal of Cancer Research and Clinical Oncology. 2023;149(10): 7997–8006. https://doi.org/10.1007/s00432-023-04667-5.
77. Uddin J, Feng C, Xu J. Health Communication on the Internet: Promoting Public Health and Exploring Disparities in the Generative AI Era. Journal of Medical Internet Research. 2025;27: e66032. https://doi.org/10.2196/66032.
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