Using Generative AI to create clinical scenarios to improve diagnostic capabilities in specialized consultation for medical students

Document Type : Original Article

Author

Department of Psychology, Faculty of Literature and Humanities, University of Malayer, Malayer, Iran

Abstract

Background: Given the growing importance of artificial intelligence (AI) in various fields and the necessity of its integration into education, this study was designed with the aim of using Generative AI to create clinical scenarios to improve diagnostic competency in clinical counseling for medical students.
Method: This study was a quasi-experimental design. Forty students from Tehran University of Medical Sciences and Iran university of Medical Sciences were conveniently selected and randomly assigned to either an experimental or a control group. The experimental group used AI-based counseling scenarios for 24 days, while the control group received traditional training. The students' diagnostic abilities were measured in terms of accuracy, speed, and differentiation before and after the intervention using a validated and reliable smart tool. All analyses were performed with SPSS 24 software.
Results: Based on the study's findings, using Generative AI to create clinical scenarios significantly improved the diagnostic competency of medical students. This novel educational approach was more effective than the traditional method across all dimensions examined, including correct diagnosis (F1,38=8.81,P<0.001), diagnostic speed (F1,38=5.49,P<0.001), differentiation ability (F1,38=6.22,P<0.001), and overall diagnostic competency (F1,38=13.44,P<0.001).
Conclusion: The use of Generative AI is an effective strategy for improving diagnostic competency in clinical counseling for medical students.
Key Words: Artificial Intelligence, Clinical Competence, Diagnosis, Reaction Time, Education, Medical

Keywords

Main Subjects


  1. Bhuyan SS, Sateesh V, Mukul N, Galvankar A, Mahmood A, Nauman M, et al. Generative Artificial Intelligence Use in Healthcare: Opportunities for Clinical Excellence and Administrative Efficiency. J Med Syst. 2025;49(1):10. doi:10.1007/s10916-024-02136-1
  2. Samuel J. A Call for Proactive Policies for Informatics and Artificial Intelligence Technologies. Scholars Strategy Network; 2021. Available from: https://scholars.org/contribution/call-proactive-policies-informatics-and. Accessed May 31, 2024.
  3. Foundation models, generative AI, and large language models in healthcare: An overview. Comput Inform Nurs. 2024;42(5). Available from: https://journals.lww.com/cinjournal/fulltext/2024/05000/foundation_models,_generative_ai,_and_large.11.aspx. Accessed May 31, 2024.
  4. Reddy S. Generative AI in healthcare: an implementation science informed translational path on application, integration and governance. Implementation Sci. 2024;19(27). https://doi.org/10.1186/s13012-024-01357-9
  5. Brynjolfsson E, Li D, Raymond LR. Generative AI at Work, NBER Working Papers 31161, National Bureau of Economic Research, Inc. 2023.
  6. Suthar AC, Joshi V, Prajapati R. A review of generative adversarial-based networks of machine learning/artificial intelligence in healthcare. 2022.
  7. Kanjee Z, Crowe B, Rodman A. Accuracy of a generative artificial intelligence model in a complex diagnostic challenge. JAMA. 2023;330:78–80.
  8. Vert JP. How will generative AI disrupt data science in drug discovery? Nat Biotechnol. 2023;41(6):750–1.
  9. Zhavoronkov A. Caution with AI-generated content in biomedicine. Nat Med. 2023;29(3):532.
  10. Zohny H, McMillan J, King M. Ethics of generative AI. J Med Ethics. 2023;49(2):79–80.
  11. Bragazzi NL, Garbarino S. Toward Clinical Generative AI: Conceptual Framework. JMIR AI. 2024; 3, e55957. https://doi.org/10.2196/55957
  12. Young M, Thomas A, Lubarsky S, Ballard T, Gordon D, Gruppen LD, et al. Drawing boundaries: the difficulty in defining clinical reasoning. Acad Med. 2018;93(7):990–5.
  13. Young ME, Thomas A, Lubarsky S, Gordon D, Gruppen LD, Rencic J, et al. Mapping clinical reasoning literature across the health professions: a scoping review. BMC Med Educ. 2020;20(1):107.
  14. Andreoletti M, Berchialla P, Boniolo G, Chiffi D. Introduction: foundations of clinical reasoning—an epistemological stance. Topoi. 2018;38(2):389–94. doi: 10.1007/s11245-018-9619-4.
  15. Chiffi D. Clinical Reasoning: Knowledge, Uncertainty, and Values in Health Care. Cham, Switzerland: Springer International Publishing; 2020.
  16. Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. Hillsdale, NJ: Lawrence Erlbaum Associates; 1988.
  17. Maxwell SE, Delaney HD. Designing experiments and analyzing data: A model comparison perspective. Mahwah, NJ: Lawrence Erlbaum Associates; 2004.
  18. Green SB. How many subjects does it take to do a regression analysis. Multivariate Behav Res. 1991;26(3):499-510. doi:10.1207/s15327906mbr2603_7
  19. Stevens JP. Applied multivariate statistics for the social sciences. 4th ed. Mahwah, NJ: Lawrence Erlbaum Associates; 2002.
  20. Malmir M,  Salehi  K,  Moghadamzadeh  A, Dehghani  Commonality and differentiation of the concept of evaluation culture in university systems in order to develop human-social capital. Social Capital Management, 2024; 11(2): 133-47. doi: 10.22059/jscm.2023.364330.2441
  21. Malmir M,  Salehi  K,  Moghadamzadeh  A, Dehghani  Construction of a Standardized Questionnaire to measure the Culture of evaluation in the Higher Education System: A mixed research of instrument development model. Organizational Culture Management, 2024; 12: -. doi: 10.22059/jomc.2024.371651.1008636
  22. Malmir M, Zare M, Sarikhani R, Mansouri V, Salari M. The Impact of Using E-Portfolio on Nursing Students' Learning in Physiology Course. Future Med Educ J. 2016; 6(2): 9-12. doi: 10.22038/fmej.2016.7155
  23. Preiksaitis C, Rose C. Opportunities, Challenges, and Future Directions of Generative Artificial Intelligence in Medical Education: Scoping Review. J Med Internet Res. 2023;9(48785):e48785.
  24. Foote HP, Hong C, Anwar M, Borentain M, Bugin K, Dreyer N, et al. Embracing Generative Artificial Intelligence in Clinical Research and Beyond: Opportunities, Challenges, and Solutions. JACC Adv. 2025;4(3):101593.
  25. Pinaya WH, Graham MS, Kerfoot E, Daniel Tudosiu P, Dafflon J, Fernandez, V, et al. Generative AI for medical imaging: extending the MONAI framework. arXiv. 2023;2307.15208.
  26. Dwivedi YK, Kshetri N, Hughes L, Louise Slade E, Jeyaraj  A, Kumar Kar A, et al. Opinion paper:“So what if ChatGPT wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. Int J Inf Manag. 2023;71:102642.
  27. Zeng X, Wang F, Luo Y, Kang SG, Tang J, Lightstone FC, et al. Deep generative molecular design reshapes drug discovery. Cell Rep Med. 2022;3(12):100794.
  28. Vora LK, Gholap AD, Jetha K, Thakur RRS, Solanki HK, Chavda VP. Artificial intelligence in pharmaceutical technology and drug delivery design. Pharmaceutics. 2023;15(7):1916.
  29. Reddy S. Generative AI in healthcare: an implementation science informed translational path on application, integration and governance. Implement Sci. 2024;19(1):27.
  30. Cho YS. From code to cure: unleashing the power of generative artificial intelligence in medicine. Int Neurourol J. 2023;27(4):225.