Horizon of Medical Education Development

Current Issue

By Issue

By Author

By Subject

Author Index

Keyword Index

Questionnaires of Articles

About Journal

Aims and Scope

Editorial Board

Names of Reviewers

Publication Ethics

Editorial policy

Peer Review Process

Advertising policy

Indexing and Abstracting

FAQ

Corrections, Retractions and Matters Arising

Authors General Guideline

Guide for Reviewers

Guideline of preparing Author Contribition

Audio Abstract Setting Guideline

Forms

Generative Artificial Intelligence in Medical Education: Model Design Based on the Philosophy of Subjectivism with the Meta-synthesis Method

,

Document Type : Original Article
10.22038/hmed.2025.86972.1509

Abstract

Introduction: In the philosophical perspective of medical education, generative artificial intelligences such as ChatGPT are an unprecedented opportunity to improve the clinical performance of medical professors and students. The aim of this research is to present a model for the application of generative artificial intelligence in medical education based on the philosophy of subjectivism with a meta-synthesis approach.
Materials & Methods:  The present study was applied in terms of purpose; qualitative in terms of data nature; and meta-synthesis research with the seven-step procedure of Sandolowski and Barso (2006). Specific keywords were collected in domestic and foreign databases; magazines Noor, Magiran, Civilica, Ganj; Elmnet; IJET, Eric, Scopus, Science Direct, and Google Scholar. After several stages of screening, 19 study units were finally selected, and then, through coding and content analysis, a 5-component model was developed.
Results: Studies showed that the resulting model, including the stages of philosophical background and ethical orientation, design and development, validation and critical evaluation, controlled application and integration into the curriculum, and continuous evaluation and revision based on five categories: duality, rationality and thinking, mechanistic world, methodological skepticism, and ethical considerations, can increase the accuracy of diagnosis and decision-making of medical students in various clinical settings.
Conclusion: The results show that the proposed model can meet the diverse needs of medical faculty and students in clinical settings, especially in the area of ​​active learning, and seamlessly adapts to the evolving demands of today's humans. Finally, the integration of philosophical foundations with generative artificial intelligence represents a new approach that equips humans with the skills and knowledge to thrive in the rapidly changing future landscape of global medical education.

Keywords

References
  1. Chen JJ, Perez C. Enhancing assessment and personalized learning through artificial intelligence. Childhood Education. 2023; 99(6):72-9.
  2. Chan KS, Zary N. Applications and challenges of implementing artificial intelligence in medical education: integrative review. JMIR medical education. 2019; 5(1):13-30.
  3. Ali M. The Role of AI in Reshaping Medical Education: Opportunities and Challenges. The Clinical Teacher. 2025; 22(2):70-140.
  4. Ooi KB, Tan GW, Al-Emran M, Al-Sharafi MA, Capatina A, Chakraborty A, Dwivedi YK, Huang TL, Kar AK, Lee VH, Loh XM. The potential of generative artificial intelligence across disciplines: Perspectives and future directions. Journal of Computer Information Systems. 2025; 65(1):76-107.
  5. Moradimokhles H, Hwang GJ, Zangeneh H, Pourjamshidi M. Study of Deep Learning in Medical Education: Opportunities, Achievements and Future Challenges. Journal of Advances in Medical Education & Professionalism. 2024; 12(3):148-62.
  6. Li Y, Li J. Generative artificial intelligence in medical education: way to solve the problems. Postgraduate Medical Journal. 2024; 100(1181):203-4.
  7. Maidaniuk I, Tetiana TS, Hoian I, Doichyk M, Patlaichuk O, Stupak O. The problem of artificial intelligence in contemporary philosophy. BRAIN. Broad Research in Artificial Intelligence and Neuroscience. 2022; 13(4):436-49.
  8. Segev A. Subjectivity as the Purpose of Education and Teaching. Studies in Philosophy and Education. 2024; 43(3):269-87.
  9. Azar NG, Yazdani S, Khoshgoftar Z. The Critical Thinking Process: A Holistic View to Promote Critical Problem-solving in Health Profession Education. Journal of Medical Education. 2023; 22(1):35-39.
  10. Stephan KD, Klima G. Artificial intelligence and its natural limits. AI & SOCIETY. 2021; 36(1):9-18.
  11. Bishop JM. Artificial intelligence is stupid and causal reasoning will not fix it. Frontiers in Psychology. 2021; 11(7):513-474.
  12. Kolides A, Nawaz A, Rathor A, Beeman D, Hashmi M, Fatima S, Berdik D, Al-Ayyoub M, Jararweh Y. Artificial intelligence foundation and pre-trained models: Fundamentals, applications, opportunities, and social impacts. Simulation Modelling Practice and Theory. 2023; 12(6):102-754.
  13. Sun L, Yin C, Xu Q, Zhao W. Artificial intelligence for healthcare and medical education: a systematic review. American Journal of Translational Research. 2023; 15(7):4820-8.
  14. Sallam M. ChatGPT utility in healthcare education, research, and practice: systematic review on the promising perspectives and valid concerns. Healthcare (Basel). 2023; 11 (6): 887. Nature. 2023; 616(7956):259-65.
  15. Gabriel I. Artificial intelligence, values, and alignment. Minds and machines. 2020; 30(3):411-37.
  16. Sandelowski M, Barroso J, Voils CI. Using qualitative metasummary to synthesize qualitative and quantitative descriptive findings. Research in nursing & health. 2007; 30(1):99-111.
  17. Tong A, Flemming K, McInnes E, Oliver S, Craig J. Enhancing transparency in reporting the synthesis of qualitative research: ENTREQ. BMC medical research methodology. 2012; 12(1):1-8.
  18. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Moher D. Updating guidance for reporting systematic reviews: development of the PRISMA 2020 statement. Journal of clinical epidemiology. 2021;13(4):103-12.
  19. DUTTA D. Descartes' Legacy in AI Ethics: Reconsidering Cartesian Dualism in Conceptualizing Ethics of AI. The Interdisciplinary Journal of Human and Social Studies. 2024; 3(2):15-27.
  20. Mir MM, Mir GM, Raina NT, Mir SM, Mir SM, Miskeen E, Alharthi MH, Alamri MM. Application of artificial intelligence in medical education: current scenario and future perspectives. Journal of advances in medical education & professionalism. 2023; 11(3):133-140.
  21. Wartman SA, Combs CD. Reimagining medical education in the age of AI. AMA journal of ethics. 2019; 21(2):146-52.
  22. Saba NU, Faheem M. Types of Artificial Intelligence and Future of Artificial Intelligence in Medical Sciences. InArtificial Intelligence in Medicine and Surgery-An Exploration of Current Trends, Potential Opportunities, and Evolving Threats. 2023; 6(10):70-5.
  23. McCoy LG, Nagaraj S, Morgado F, Harish V, Das S, Celi LA. What do medical students actually need to know about artificial intelligence?. NPJ digital medicine. 2020; 3(1): 33-41.
  24. Raphael MW. Artificial intelligence and the situational rationality of diagnosis: human problem‐solving and the artifacts of health and medicine. Sociology Compass. 2022; 6(10):130-47.
  25. Buckner C. Deeply Rational Machines--What the History of Philosophy Can Teach Us about the Future of Artificial Intelligence. Moderate Empiricism and Machine Learning. 2023; 24(6):163-201.
  26. Brown DJ. Nature, Artifice, and Discovery in Descartes’ Mechanical Philosophy. Philosophies. 2023; 8(5):8-15.
  27. Busch F, Adams LC, Bressem KK. Biomedical ethical aspects towards the implementation of artificial intelligence in medical education. Medical science educator. 2023; 33(4):1007-12.
  28. Ijaz F. Integration of Artificial Intelligence Technology in Medical Education. MedERA-Journal of CMH LMC and IOD. 2023; 4(23):123-154.
  29. O’Regan G. History of artificial intelligence. A Brief History of Computing: Springer; 2021; 71(9):295-319.
  30. Dumitrache T. Artificial Intelligence: Between Sense And Sensibility–The Question Of Social Reluctance. Limba Și Literatura–Repere Identitare În Context European. 2020; 41(26):139-48.
  31. Cornelio C, Dash S, Austel V, Josephson T, Goncalves J, Clarkson K, et al. AI Descartes: Combining data and theory for derivable. scientific discovery. 2021; 9(1):16-34.
  32. Chursinova O, Stebelska O. Is the Realization of the Emotional Artificial Intelligence Possible? Philosophical and Methodological Analysis. Filosofija. Sociologija. 2021; 32(1):34-51.
  33. Schwartz DG. Review of Knowing our world: an artificial intelligence perspective. Springer, 2021; 12(5):65-79.
  34. Zimmerli WC. Deus Malignus–The Digital Rehabilitation of Deception. Artificial Intelligence: Brill mentis; 2020; 38(1):15-35.
  35. Paranjape K, Schinkel M, Panday RN, Car J, Nanayakkara P. Introducing artificial intelligence training in medical education. JMIR medical education. 2019; 38(1):185-96.
  36. Masters K. Ethical use of artificial intelligence in health professions education: AMEE Guide No. 158. Medical Teacher. 2023; 45(6):574-84.
  37. Knopp MI, Warm EJ, Weber D, Kelleher M, Kinnear B, Schumacher DJ, Santen SA, Mendonça E, Turner L. AI-enabled medical education: threads of change, promising futures, and risky realities across four potential future worlds. JMIR Medical Education. 2023; 2(9):503-73.
  38. McCarthy J. Artificial intelligence, logic, and formalising common sense. Machine Learning and the City: Applications in Architecture and Urban Design. 2022; 17(2):69-90.
  39. Kauffman SA, Roli A. What is consciousness? Artificial intelligence, real intelligence, quantum mind and qualia. Biological Journal of the Linnean Society. 2023; 139(4):530-8.
  40. Asadi H, Yousefian M, Mehri S. The State of Spiritual Intelligence in Iranian Nurses: A Systematic Review. JRUMS. 2025; 23(8):721-737. [In Persian]
  41. Rezaei G, Nasseri A, Maazallahi H. Investigating the Correlation between Public Hospitals Managers' Accountability Pressure in Kerman City and Budgetary Slack with Focusing on the Role of Moral Intelligence and Information Asymmetry in 2022: A Descriptive Study. JRUMS. 2024; 22(11)1163-1178. [In Persian]
  42. Motaarefi H, Sakhaei S, Ashrafi M, Zinalpoor S, Gholizadgougjehyaran H. The Effect of Smartphone-based Training of Triage the Knowledge and Decision Making of Emergency Nurses. JRUMS. 2022; 21(2):137-148. [In Persian]
  43. Roozbahani N. Design, Implementation, and Evaluation of a Virtual Peer Education Program. Horizon of Medical Education Development. 2024; 15(1):6-15. [In Persian]
  44. Rajabpour M, Karimi Moonaghi H. Effective Tools and Methods for Evaluating the Clinical Performance of Medical Sciences Students: A Systematic Review. Horizon of Medical Education Development. 2024; 15(2):69-81. [In Persian]
  45. Nili Ahmadabadi MR, Mohtadi S. Educational Agent's Role and Learning. Horizon of Medical Education Development. 2021; 12(2):105-97. [In Persian]
  46. Kung TH, Cheatham M, Medenilla A, Sillos C, De Leon L, Elepaño C, Madriaga M, Aggabao R, Diaz-Candido G, Maningo J, Tseng V. Performance of ChatGPT on USMLE: potential for AI-assisted medical education using large language models. PLOS Digit Health 2023; 2(2):19-8.
  47. Abd-Alrazaq A, AlSaad R, Alhuwail D, Ahmed A, Healy PM, Latifi S, Aziz S, Damseh R, Alrazak SA, Sheikh J. Large language models in medical education: opportunities, challenges, and future directions. JMIR Medical Education. 2023; 9(1):48-291.
  48. Ghosh A, Bir A. Evaluating ChatGPT’s ability to solve higher-order questions on the competency-based medical education curriculum in medical biochemistry. Cureus. 2023; 15(4): 370-23
  49. Chun KH, Jin HK, Yoon JH, Kim MG, Choi KH, Kim E, Kim H, Kim JK, Kim G, Kim K, Lee JY. Novel innovative computer-based test (Inno-CBT) item types for national licensing examinations for health care professionals. BMC Medical Education. 2023; 23(1):5-60.
Horizon of Medical Education Development
Volume 16, Special Issue1
August 2025
Pages 88-101
Files
Share
How to cite
Statistics