Evidence map›Paper›PMID 39979912›Full record

ArticleBMC medical education2025

Navigating the integration of artificial intelligence in the medical education curriculum: a mixed-methods study exploring the perspectives of medical students and faculty in Pakistan.

Muhammad Ahsan Naseer, Sana Saeed, Azam Afzal, Sobia Ali, Marib Ghulam Rasool Malik

Abstract read
In one paragraph

Article in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 4 pooled it
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

26 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Muhammad Ahsan NaseerDepartment of Health Professions Education, Liaquat National Hospital & Medical College, Karachi, Pakistan.
Sana SaeedDepartment of Paediatrics and Child Health, Aga Khan University, Stadium Road, Karachi, Pakistan. sana.saeed@aku.edu.
Azam AfzalDepartment of Educational Development, Aga Khan University, Karachi, Pakistan.
Sobia AliDepartment of Health Professions Education, Liaquat National Hospital & Medical College, Karachi, Pakistan.
Marib Ghulam Rasool MalikDean's Clinical Research Fellowship, Aga Khan University, Karachi, Pakistan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe integration of artificial intelligence (AI) into medical education is poised to revolutionize teaching, learning, and clinical practice. However, successful implementation of AI-based tools in medical curricula faces several challenges, particularly in resource-limited settings like Pakistan, where technological and institutional barriers remain significant. This study aimed to evaluate knowledge, attitudes, and practices of medical students and faculty regarding AI in medical education, and explore the perceptions and key barriers regarding strategies for effective AI integration.

methodsA concurrent mixed-methods study was conducted over six months (July 2023 to January 2024) at a tertiary care medical college in Pakistan. The quantitative component utilized a cross-sectional design, with 236 participants (153 medical students and 83 faculty members) completing an online survey. Mean composite scores for knowledge, attitudes, and practices were analyzed using non-parametric tests. The qualitative component consisted of three focus group discussions with students and six in-depth interviews with faculty. Thematic analysis was performed to explore participants' perspectives on AI integration.

resultsMajority of participants demonstrated a positive attitude towards AI integration. Faculty had significantly higher mean attitude scores compared to students (3.95 ± 0.63 vs. 3.81 ± 0.75, p = 0.040). However, no statistically significant differences in knowledge (faculty: 3.53 ± 0.66, students: 3.55 ± 0.73, p = 0.870) or practices (faculty: 3.19 ± 0.87, students: 3.23 ± 0.89, p = 0.891) were found. Older students reported greater self-perceived knowledge (p = 0.010) and more positive attitudes (p = 0.016) towards AI, while male students exhibited higher knowledge scores than females (p = 0.025). Qualitative findings revealed key themes, including AI's potential to enhance learning and research, concerns about over-reliance on AI, ethical issues surrounding privacy and confidentiality, and the need for institutional support. Faculty emphasized the importance of training to equip educators with the necessary skills to effectively integrate AI into their teaching.

conclusionsThis study highlights both the enthusiasm for AI integration and the significant barriers that must be addressed to successfully implement AI in medical education. Addressing technological constraints, providing faculty training, and developing ethical guidelines are critical steps toward fostering the responsible use of AI in medical curricula. These findings underscore the need for context-specific strategies, particularly in resource-limited settings, to ensure that medical students and educators are well-prepared for the future of healthcare.

Indexed as

Artificial IntelligenceAttitude of Health PersonnelCurriculumEducation, MedicalFaculty, MedicalStudents, MedicalAdultCross-Sectional StudiesEducation, Medical, UndergraduateFemaleFocus GroupsHealth Knowledge, Attitudes, PracticeHumansMalePakistanSurveys and QuestionnairesArtificial intelligenceCurriculumDeep learningMedical education

Identifiers

PMID39979912
PMCPMC11844081

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.