EXPLORING FACULTY PERCEPTIONS REGARDING INTEGRATION OF ARTIFICIAL INTELLIGENCE IN MEDICAL AND DENTAL EDUCATION

Authors

  • Sarah Ali HBS Medical and Dental College, Islamabad-Pakistan
  • Anbreen Aziz HBS Medical and Dental College, Islamabad-Pakistan
  • Hina Zahoor HBS Medical and Dental College, Islamabad-Pakistan

DOI:

https://doi.org/10.55519/JAMC-S4-14120

Keywords:

Artificial intelligence, integration, medical education, dental education

Abstract

Background: Considering hype about artificial intelligence, limited research has been done regarding its integration in medical and dental education within Pakistani context. This study aims to explore faculty perceptions on integrating AI into medical and dental education. Methods: This is an exploratory qualitative study that has explored faculty perceptions on integrating Artificial Intelligence into medical and dental education. A purposive sample of 20 faculty members participated in the study. An interview guide with six semi-structured questions was utilized for data collection. After assuring anonymity and confidentiality, all interviews were audio recorded and transcribed verbatim. The transcripts were anonymized and shared with the co-authors for data analysis. Thematic analysis with a deductive approach was performed by utilizing the six-step process provided by Braun and Clarke. Results: Five main themes and fifteen subthemes emerged. The participants said that artificial intelligence has the power to completely transform medical and dental education by enhancing teaching, research and patient care. Some challenges need to be addressed that restrict wide implementation of artificial intelligence in the healthcare sector such as lack of expertise, resource constraints, and lack of understanding to use it for teaching and learning. Artificial intelligence has the potential to improve the learning environment by modifying learning experiences of students. According to the results, Institutions has a responsibility to provide comprehensive training programs for this purpose Artificial intelligence ethics must be kept in mind while integrating it into medical and dental education by creating explicit policies and offering faculty development initiatives. Conclusion: This study highlights the necessity of training programs that allow medical and dental faculty to effectively use artificial intelligence to enhance teaching and practice in healthcare. It is pertinent to keep ethics in mind while utilizing Artificial Intelligence into medical and dental education.

References

1. Telang A. Fourth Industrial revolution and health professions education. Arch Med Sci 2019;7(2):265–6.

2. Zhang W, Cai M, Lee HJ, Evans R, Zhu C, Ming CJE, et al. AI in Medical Education: Global situation, effects and challenges. Educ Inf Technol 2024;29(4):4611–33.

3. Lee D, Yoon SN. Application of artificial intelligence-based technologies in the healthcare industry: Opportunities and challenges. Int J Environ Res Public Health 2021;18(1):271.

4. Aldeman NLS, de Sá Urtiga Aita KM, Machado VP, da Mata Sousa LCD, Coelho AGB, da Silva AS, et al. Smartpathk: a platform for teaching glomerulopathies using machine learning. BMC Med Educ 2021;21(1):248.

5. Sethi A. Artificial Intelligence in Health Professions Education. J Shalamar Med Dent Coll 2024;5(1):1–3.

6. Alshadoodee HAA, Mansoor MSG, Kuba HK, Gheni HM. The role of artificial intelligence in enhancing administrative decision support systems by depend on knowledge management. Bull Electr Eng Inform 2022;11(6):3577–89.

7. Qamar W, Khaleeq N, Nisar A, Tariq SF, Lajber M. Exploring dental professionals’outlook on the future of dental care amidst the integration of artificial intelligence in dentistry: a pilot study in Pakistan. BMC Oral Health 2024;24(1):542.

8. Bajwa J, Munir U, Nori A, Williams B. Artificial intelligence in healthcare: transforming the practice of medicine. Future Healthc J 2021;8(2):e188–94.

9. 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.

10. Kim CS, Samaniego CS, Sousa Melo SL, Brachvogel WA, Baskaran K, Rulli D. Artificial intelligence (AI) in dental curricula: ethics and responsible integration. J Dent Educ 2023;87(11):1570–3.

11. Mandal R, Mete DJ. Teachers’ and students’ perception towards integration of artificial intelligence in school curriculum: A Survey. Int J Multidiscip Educ Res 2023;12(7):5.

12. Campbell S, Greenwood M, Prior S, Shearer T, Walkem K, Young S, et al. Purposive sampling: complex or simple? Research case examples. J Res Nurs 2020;25(8):652–61.

13. Artino AR, Rochelle JSLA, Dezee KJ, Gehlbach H. Developing questionnaires for educational research: AMEE Guide No. 87. Med Tech 2014;36(6):463–74.

14. Kiger ME, Varpio L. Thematic analysis of qualitative data: AMEE Guide No. 131. Med Teach 2020;42(8):846–54.

15. Chan KS, Zary N. Applications and challenges of implementing artificial intelligence in medical education: integrative review. JMIR Med Educ 2019;5(1):e13930.

16. Chen M, Zhang B, Cai Z, Seery S, Gonzalez MJ, Ali NM, et al. Acceptance of clinical artificial intelligence among physicians and medical students: a systematic review with cross-sectional survey. Front Med (Lausanne) 2022;9:990604.

17. Tong W, Zhang X, Zeng H, Pan J, Gong C, Zhang H. Reforming China’s Secondary Vocational Medical Education: Adapting to the Challenges and Opportunities of the AI Era. JMIR Med Educ 2024;10:e48594.

18. Malik A, Solaiman B. AI in hospital administration and management: Ethical and legal implications. Research Handbook on Health, AI and the Law: Edward Elgar Publishing, 2024; p.21–40.

19. Quttainah M, Mishra V, Madakam S, Lurie Y, Mark S. Cost, Usability, Credibility, Fairness, Accountability, Transparency, and Explainability Framework for Safe and Effective Large Language Models in Medical Education: Narrative Review and Qualitative Study. JMIR AI 2024;3(1):e51834.

20. Singhal A, Neveditsin N, Tanveer H, Mago VJ. Toward Fairness, Accountability, Transparency, and Ethics in AI for Social Media and Health Care: Scoping Review. JMIR Med Inform 2024;12(1):e50048.

21. Kaswan KS, Dhatterwal JS, Ojha RP. AI in personalized learning. In: Advances in Technological Innovations in Higher Education: CRC Press, 2024; p.103–17.

22. Shafi I, Ansari S, Din S, Jeon G, Paul A. Artificial neural networks as clinical decision support systems. Concurr Comput Pract Exp 2021;33(22):e6342.

23. Lu Y. Artificial intelligence: a survey on evolution, models, applications and future trends. J Manag Anal 2019;6(1):1–29.

6. Ng DTK, Leung JKL, Su J, Ng RCW, Chu SKW. Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world. Educ Technol Res Dev 2023;71(1):137–61.

24. Aung YY, Wong DCS, Ting DSW. The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare. Br Med Bull 2021;139(1):4–15.

25. Masters K. Ethical use of artificial intelligence in health professions education: AMEE Guide No. 158. Med Teach 2023;45(6):574–84.

Published

2024-12-16

How to Cite

Ali, S., Aziz, A., & Zahoor, H. (2024). EXPLORING FACULTY PERCEPTIONS REGARDING INTEGRATION OF ARTIFICIAL INTELLIGENCE IN MEDICAL AND DENTAL EDUCATION. Journal of Ayub Medical College Abbottabad, 36(4 (Suppl 1). https://doi.org/10.55519/JAMC-S4-14120