Perceptions of Artificial Intelligence in Medical Education: A Cross-Sectional Study Among Students and Faculty at HBS Medical and Dental College, Islamabad
AI in Medical Education: Study Among Students and Faculty
DOI:
https://doi.org/10.54393/pjhs.v6i5.3079Keywords:
Artificial Intelligence, Medical Education, Curriculum Integration, Faculty PerceptionAbstract
As artificial intelligence (AI) continues to transform healthcare, its integration into medical education is increasingly critical. However, many institutions lack formal AI curricula, leaving students and faculty underprepared for the digital demands of clinical practice. Objectives: To assess awareness, familiarity, perceived benefits, and concerns regarding AI among medical students and faculty, and to explore training preferences and barriers to AI integration in academic settings. Methods: A descriptive cross-sectional survey was conducted at a HBS Medical and Dental College, with a total of 100 participants (76 students and 24 faculty). A questionnaire assessed demographic characteristics, AI familiarity, perceived benefits and concerns, and interest in formal training. Chi-square tests and logistic regression were used to analyse group differences and predictors of training interest. Results: Most participants (60%) were under 25 years old, and 76% were students. While 68% had heard of AI, only 43% reported basic familiarity. Interest in AI training was high (87%). Commonly cited benefits included faster knowledge access and personalized learning, while concerns focused on ethical issues and misinformation. A significant association was found between academic role and perceived lack of training (p=0.041). Logistic regression showed a non-significant trend linking prior AI exposure with interest in training (p=0.125). Conclusions: It was concluded that there is strong enthusiasm for AI in medical education among both students and faculty. However, limited familiarity and perceived barriers highlight the need for structured training and targeted curriculum reforms to build digital competence in future healthcare professionals.
References
Oluwadiya KS, Adeoti AO, Agodirin SO, Nottidge TE, Usman MI, Gali MB et al. Exploring Artificial Intelligence in the Nigerian Medical Educational Space: An Online Cross-Sectional Study of Perceptions, Risks and Benefits Among Students and Lecturers from Ten Universities. Nigerian Postgraduate Medical Journal. 2023 Oct; 30(4): 285-92. doi: 10.4103/npmj.npmj_186_23.
Weidener L and Fischer M. Artificial Intelligence in Medicine: Cross-Sectional Study among Medical Students on Application, Education, and Ethical Aspects. Journal of Medical Internet Research Medical Education. 2024 Jan; 10(1): e51247. doi: 10.2196/51247.
Busch F, Hoffmann L, Truhn D, Ortiz-Prado E, Makowski MR, Bressem KK et al. Medical Students’ Perceptions Towards Artificial Intelligence in Education and Practice: A Multinational, Multicenter Cross-Sectional Study. medRxiv. 2023 Dec: 2023-12. doi: 10.1101/2023.12.09.23299744.
Jackson P, Ponath Sukumaran G, Babu C, Tony MC, Jack DS, Reshma VR et al. Artificial Intelligence in Medical Education-Perception among Medical Students. BioMed Central Medical Education. 2024 Jul; 24(1): 804. doi: 10.1186/s12909-024-05760-0.
Jebreen K, Radwan E, Kammoun-Rebai W, Alattar E, Radwan A, Safi W et al. Perceptions of Undergraduate Medical Students on Artificial Intelligence in Medicine: Mixed-Methods Survey Study from Palestine. BioMed Central Medical Education. 2024 May; 24(1): 507. doi: 10.1186/s12909-024-05465-4.
Almalki M, Alkhamis MA, Khairallah FM, Choukou MA. Perceived Artificial Intelligence Readiness in Medical and Health Sciences Education: A Survey Study of Students in Saudi Arabia. BioMed Central Medical Education. 2025 Mar; 25(1): 439. doi: 10.1186/s12909-025-06995-1.
Pucchio A, Rathagirishnan R, Caton N, Gariscsak PJ, Del Papa J, Nabhen JJ et al. Exploration of Exposure to Artificial Intelligence in Undergraduate Medical Education: A Canadian Cross-Sectional Mixed-Methods Study. BioMed Central Medical Education. 2022 Nov; 22(1): 815. doi: 10.1186/s12909-022-03896-5.
Buabbas AJ, Miskin B, Alnaqi AA, Ayed AK, Shehab AA, Syed-Abdul S et al. Investigating Students’ Perceptions Towards Artificial Intelligence in Medical Education. In Healthcare. 2023 May; 11(9): 1298. doi: 10.3390/healthcare11091298.
Derakhshanian S, Wood L, Arruzza E. Perceptions and Attitudes of Health Science Students Relating to Artificial Intelligence (AI): A Scoping Review. Health Science Reports. 2024 Aug; 7(8): e2289. doi: 10.1002/hsr2.2289.
Swed S, Alibrahim H, Elkalagi NK, Nasif MN, Rais MA, Nashwan AJ et al. Knowledge, Attitude, and Practice of Artificial Intelligence Among Doctors and Medical Students in Syria: A Cross-Sectional Online Survey. Frontiers in Artificial Intelligence. 2022 Sep; 5: 1011524. doi: 10.3389/frai.2022.1011524.
Civaner MM, Uncu Y, Bulut F, Chalil EG, Tatli A. Artificial Intelligence in Medical Education: A Cross-Sectional Needs Assessment. BioMed Central Medical Education. 2022 Nov; 22(1): 772. doi: 10.1186/s12909-022-03852-3.
Rani S, Kumari A, Ekka SC, Chakraborty R, Ekka S. Perception of Medical Students and Faculty Regarding the Use of Artificial Intelligence (AI) in Medical Education: A Cross-Sectional Study. Cureus. 2025 Jan; 17(1). doi: 10.7759/cureus.77514.
Liu DS, Sawyer J, Luna A, Aoun J, Wang J, Boachie L et al. Perceptions of US Medical Students on Artificial Intelligence in Medicine: Mixed Methods Survey Study. Journal of Medical Internet Research Medical Education. 2022 Oct; 8(4): e38325. doi: 10.2196/38325.
Sami A, Tanveer F, Sajwani K, Kiran N, Javed MA, Ozsahin DU et al. Medical Students’ Attitudes Toward AI in Education: Perception, Effectiveness, and Its Credibility. BioMed Central Medical Education. 2025 Jan; 25(1): 82. doi: 10.1186/s12909-025-06704-y.
Yañez OJ, Kim J, Montalvo F, Romero MV. Perspectivas Estudiantiles y Docentes sobre la IA en la Educación Sanitaria: Scoping Review. Revista Española de Educación Médica. 2025 Feb; 6(2). doi: 10.6018/edumed.633811.
Saleh ZT, Rababa M, Elshatarat RA, Alharbi M, Alhumaidi BN, Al-Za’areer MS et al. Exploring Faculty Perceptions and Concerns Regarding Artificial Intelligence Chatbots in Nursing Education: Potential Benefits and Limitations. BioMed Central Nursing. 2025 Apr; 24(1): 440. doi: 10.1186/s12912-025-03082-0.
Abouammoh N, Alhasan K, Aljamaan F, Raina R, Malki KH, Altamimi I et al. Perceptions and Earliest Experiences of Medical Students and Faculty with ChatGPT in Medical Education: Qualitative Study. Journal of Medical Internet Research Medical Education. 2025 Feb; 11: e63400. doi: 10.2196/63400.
Nevárez Montes J and Elizondo-Garcia J. Faculty Acceptance and Use of Generative Artificial Intelligence in Their Practice. In Frontiers in Education. 2025 Feb; 10: 1427450. doi: 10.3389/feduc.2025.1427450.
Kong SC, Yang Y, Hou C. Examining Teachers’ Behavioural Intention of Using Generative Artificial Intelligence Tools for Teaching and Learning Based on the Extended Technology Acceptance Model. Computers and Education: Artificial Intelligence. 2024 Dec; 7: 100328. doi: 10.1016/j.caeai.2024.100328.
Yilmaz FG, Yilmaz R, Ceylan M. Generative Artificial Intelligence Acceptance Scale: A Validity and Reliability Study. International Journal of Human–Computer Interaction. 2024 Dec; 40(24): 8703-15. doi: 10.1080/10447318.2023.2288730.
Al-Qahtani AS, Al-Garni AM, Almohaya AY. Applications of Generative Artificial Intelligence (AI) in Medical Education: A Current Application and Future Prospects in Saudi Arabia. 2024. doi: 10.21203/rs.3.rs-4150369/v1.
Khlaif ZN, Ayyoub A, Hamamra B, Bensalem E, Mitwally MA, Ayyoub A et al. University Teachers’ Views on the Adoption and Integration of Generative AI Tools for Student Assessment in Higher Education. Education Sciences. 2024 Oct 6; 14(10): 1090. doi: 10.3390/educsci14101090.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Pakistan Journal of Health Sciences

This work is licensed under a Creative Commons Attribution 4.0 International License.
This is an open-access journal and all the published articles / items are distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For comments