Development and Use of AI-Assisted Case-Based Learning in Dental and Medical Education
Resumen
El razonamiento clínico y las competencias diagnósticas se reconocen ampliamente comocomponentes esenciales en la educación médica y odontológica, aunque continúan siendo difícilesde desarrollar con resultados eficaces. El Aprendizaje Basado en Casos ha sido adoptado como unenfoque pedagógico estructurado para abordar diversos desafíos al involucrar a los estudiantes enescenarios clínicos reales. En los últimos años, se han introducido herramientas asistidas porinteligencia artificial, especialmente aquellas diseñadas para el desarrollo de casos clínicos y lacuración de información, con el fin de apoyar el sin sustituir los métodos de enseñanzatradicionales. Esta revisión narrativa sintetiza la literatura actual para examinar el papel de losenfoques asistidos por AI en la mejora de la experiencia de aprendizaje de los estudiantes depregrado, en términos de participación, motivación, integración del conocimiento y fortalecimientodel razonamiento diagnóstico dentro del marco del. La presente revisión también destacaconsideraciones prácticas para los educadores médicos y odontológicos, así como para los diseñadores curriculares, en cuanto a la integración de herramientas asistidas por como un mediopara reforzar las prácticas educativas clínicas, preservando la integridad educativa y los resultadoscentrados en el estudiante en la educación superior.
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1. Masters K. Artificial intelligence in medical education. Med Teach. 2019, 41(9), 976–80. https://doi.org/10.1080/0142159x.2019.1595557
2. Schwendicke F, Samek W, Krois J. Artificial intelligence in dentistry: chances and challenges. J Dent Res. 2020, 99(7), 769–74. https://doi.org/10.1177/0022034520915714
3. Moro C, Phelps C, Stromberga Z. Utilizing serious games for physiology and anatomy learning and revision. Adv Physiol Educ. 2020, 44(3), 505–7. https://doi.org/10.1152/advan.00074.2020
4. Kyaw BM, Saxena N, Posadzki P, et al. Virtual reality for health professions education: systematic review and meta-analysis. J Med Internet Res. 2019, 21(1), e12959. https://doi.org/10.2196/12959
5. Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019, 6(2), 94–8. https://doi.org/10.7861/futurehosp.6-2-9
6. Liu Q, Peng W, Zhang F, Hu R, Li Y, Yan W. The effectiveness of blended learning in health professions: systematic review and meta-analysis. J Med Internet Res. 2020, 18(1), e2. https://doi.org/10.2196/jmir.4807
7. Im SM, Kim JH, Kim S, et al. Comparative study of 360° virtual reality and traditional two-dimensional video in dental radiology classes. BMC Med Educ. 2023, 23(1), 871. https://doi.org/10.1186/s12909-023-04851-8
8. Hu C, Li F, Wang S, Gao Z, Pan S, Qing M. The role of artificial intelligence in enhancing personalized learning pathways and clinical training in dental education. Cogent Educ. 2025, 12(1). https://doi.org/10.1080/2331186X.2025.2490425
9. Sapci AH, Sapci HA. Artificial intelligence education and tools for medical and health informatics students: systematic review. JMIR Med Educ. 2020, 6(1), e19285. https://doi.org/10.2196/19285
10. Temsah O, Khan SA, Chaiah Y, Senjab A, Alhasan K, Jamal A, et al. Overview of early ChatGPT's presence in medical literature: insights from a hybrid literature review by ChatGPT and human experts. Cureus. 2023, 15(4), e37281. https://doi.org/10.7759/cureus.37281
11. Chan KS, Zary N. Applications and challenges of implementing artificial intelligence in medical education: integrative review. JMIR Med Educ. 2019, 5(1), e13930. https://doi.org/10.2196/13930
12. Li YS, Lam CSN, See C. Using a machine learning architecture to create an AI-powered chatbot for anatomy education. Med Sci Educ. 2021, 31(6), 1729–30. https://doi.org/10.1007/s40670-021-01405-9
13. Mirchi N, Bissonnette V, Yilmaz R, Ledwos N, Winkler-Schwartz A, et al. The virtual operative assistant: an explainable artificial intelligence tool for simulation-based training in surgery and medicine. PLoS One. 2020, 15(2), e0229596. https://doi.org/10.1371/journal.pone.0229596
14. Ghorashi N, Ismail A, Ghosh P, Sidawy A, Javan R. AI-powered chatbots in medical education: potential applications and implications. Cureus. 2023, 15(8), e43271. https://doi.org/10.7759/cureus.43271
15. Zeng Q, Wang K, Liu W, et al. Efficacy of high-fidelity simulation in advanced life support training: a systematic review and meta-analysis of randomized controlled trials. BMC Med Educ. 2023, 23(1), 664. https://doi.org/10.1186/s12909-023-04654-x
16. Khan RA, Jawaid M, Khan AR, Sajjad M. ChatGPT - reshaping medical education and clinical management. Pak J Med Sci. 2023, 39(2), 605–7. https://doi.org/10.12669/pjms.39.2.7653
17. Ali K, Barhom N, Tamimi F, Duggal M. ChatGPT-a double-edged sword for healthcare education? Implications for assessments of dental students. Eur J Dent Educ. 2024, 28(1), 206–11. https://doi.org/10.1111/eje.12937
18. Salloum A, Alfaisal R, Salloum SA. Revolutionizing medical education: empowering learning with ChatGPT. Cham: Springer International Publishing; 2024, p. 79–90. https://link.springer.com/chapter/10.1007/978-3-031-52280-2_6
19. Coşkun Ö, Kıyak YS, Budakoğlu Iİ. ChatGPT to generate clinical vignettes for teaching and multiple-choice questions for assessment: a randomized controlled experiment. Med Teach. 2025, 47(2), 268–74. https://doi.org/10.1080/0142159x.2024.2327477
20. Razak NIA, Yusoff MFBM, Rahmat RWOK. ChatGPT review: a sophisticated chatbot models in medical & health-related teaching and learning. Mal J Med Health Sci. 2023, 19(s12), 12. https://medic.upm.edu.my/upload/dokumen/2023112812174812_2023-0616.pdf
21. Rezigalla AA. AI in medical education: uses of AI in construction type A MCQs. BMC Med Educ. 2024, 24(1), 247. https://doi.org/10.1186/s12909-024-05250-3
22. Rahad K, Martin K, Amugo I, Ferguson SL, Curtis A, Davis A, et al. ChatGPT to enhance learning in dental education at a historically black medical college. Dent Res Oral Health. 2024, 7(1), 8–14. https://doi.org/10.26502/droh.0069
23. Thurzo A, Strunga M, Urban R, Surovková J, Afrashtehfar KI. Impact of artificial intelligence on dental education: a review and guide for curriculum update. Educ Sci. 2023, 13(2), 150. https://doi.org/10.3390/educsci13020150
24. Or AJ, Sukumar S, Ritchie HE, Sarrafpour B. Using artificial intelligence chatbots to improve patient history taking in dental education (pilot study). J Dent Educ. 2024, 88(Suppl 3), 1988–90. https://doi.org/10.1002/jdd.13591
25. Wimalarathna A. Revolutionizing dental education: harnessing the power of ChatGPT for personalized learning in dentistry. J Dent Sci Educ. 2024, 2(2), 40–2. https://doi.org/10.51271/JDSE-0031
26. Adnan K, Fahimullah, Farrukh U, Askari H, Siddiqui S, Jameel RA. AI-enabled virtual reality systems for dental education. Int J Health Sci. 2023, 7(S1), 1378–92. https://doi.org/10.53730/ijhs.v7nS1.14350
27. Lakshan MTD, Chandratilake M, Drahaman AMP, Perera MB. Exploring the pros and cons of integrating artificial intelligence and ChatGPT in medical education: a comprehensive analysis. Ceylon J Otolaryngol. 2024, 13(1), 5380.https://doi.org/10.4038/cjo.v13i1.5380
28. Hui Z, Zewu Z, Jiao H, Yu C. Application of ChatGPT-assisted problem-based learning teaching method in clinical medical education. BMC Med Educ. 2025, 25(1), 50. https://doi.org/10.1186/s12909-024-06321-1
29. Holderried F, Stegemann-Philipps C, Herschbach L, et al. A language model–powered simulated patient with automated feedback for history taking prospective study. JMIR Med Educ. 2024, 10, e59213. https://doi.org/10.2196/59213
30. Thorat VA, Joshi N, Talreja P, Shetty A. The role of chatbot GPT technology in undergraduate dental education. Cureus. 2024, 16(2), e54193. https://doi.org/10.7759/cureus.54193
31. Aster A, Laupichler MC, Rockwell-Kollmann T, et al. ChatGPT and other large language models in medical education — scoping literature review. Med Sci Educ. 2025, 35, 555–67. https://doi.org/10.1007/s40670-024-02206-6
32. Suárez AM, Adanero A, Díaz-Flores García V, Freire Y, Algar J. Using a virtual patient via an artificial intelligence chatbot to develop dental students’ diagnostic skills. Int J Environ Res Public Health. 2022, 19(14), 8735. https://doi.org/10.3390/ijerph19148735
33. Sartania N, Sneddon S, Boyle JG, McQuarrie E, de Koning HP. Increasing collaborative discussion in case-based learning improves student engagement and knowledge acquisition. Med Sci Educ. 2022, 32(5), 1055–64. https://doi.org/10.1007/s40670-022-01614-w
34. El-Hakim M, Anthonappa RP, Fawzy A. Artificial intelligence in dental education: a scoping review of applications, challenges, and gaps. Dent J. 2025, 13(9), 384. https://doi.org/10.3390/dj13090384
35. Kanathila H, Patil S, Patwardhan KS, Jadhav SB. Enhancing dental education through case-based learning - an overview. J Oral Res Rev. 2025, 17(2), 183–8. https://doi.org/10.4103/jorr.jorr_25_25
36. Kononowicz AA, Woodham LA, Edelbring S, et al. Virtual patient simulations in health professions education: systematic review and meta-analysis by the digital health education collaboration. J Med Internet Res. 2019, 21(7), e14676. https://doi.org/10.2196/14676
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