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.
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.
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.
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.
Who cites it
26 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Medical Students' Attitudes, Perceptions, and Self-Reported Familiarity With AI in Health Care: Systematic Review and Meta-Analysis.JMIR medical education · 2026Pooled it
- Content and structural needs assessment for an artificial intelligence education mobile app in healthcare: a mixed methods study.BMC medical education · 2025Pooled it
- Artificial intelligence (AI) for social innovation in health education: promoting health literacy through personalized ai-driven learning tools - a systematic review.BMC medical education · 2025Pooled it
- Integrating artificial intelligence into medical education: a narrative systematic review of current applications, challenges, and future directions.BMC medical education · 2025Pooled it
- Effectiveness of ChatGPT and DeepSeek in Urology Medical Education: Randomized Controlled Trial.Journal of medical Internet research · 2026Trial
- Integrating large language models into prostate cancer training: evidence from comparative benchmarking and a pilot randomized trial.BMC medical education · 2026Trial
- From adoption to interaction: examining human-GenAI engagement patterns, ethical tensions, and educational trade-offs in nursing education.BMC nursing · 2026Article
- Pilot of a blended learning model on medical students' communication skills in the context of digital innovations.BMC medical education · 2026Article
- Real-time interaction with artificial intelligence and its association with learning experience and academic performance among undergraduate nursing students in Peshawar: an analytical cross-sectional study.BMC medical education · 2026Article
- Effectiveness and students' perception of tutor-guided AI navigation in undergraduate medical education in Sri Lanka: a quasi-experimental study.BMC medical education · 2026Article
- The AI adoption paradox in Chinese medical education: a multi-institution cross-sectional study of usage patterns, critical literacy gaps, and adoption profiles among 3,194 undergraduate medical students.BMC medical education · 2026Article
- The AI adaptation divide in medical education: A generational perspective.PLOS digital health · 2026Article
- Performance of 5 AI Models on United States Medical Licensing Examination Step 1 Questions: Comparative Observational Study.JMIR AI · 2026Article
- Familiarity, attitude and practice of postgraduate health science students of Pakistan regarding the implication of artificial intelligence in research: an analytical survey.BMC medical education · 2026Article
- Comparative performance evaluation of ChatGPT-4 Omni and Gemini Advanced in the Turkish Dentistry Specialization Exam.BMC medical education · 2026Article
- AI literacy in undergraduate medical education: a competency-based interpretive framework for curriculum and assessment.Frontiers in medicine · 2026Review
- Application of Artificial Intelligence in Medical Education: A Systematic and Narrative Review of Pedagogical Potential and Ethical Implications.Advances in medical education and practice · 2026Review
- Perceived knowledge, attitude, and practice of artificial intelligence among medical students in Guangxi: a cross-sectional study.Frontiers in public health · 2026Article
- Article
- Twelve tips for developing and implementing AI curriculum for undergraduate medical education.Medical education online · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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
Identifiers
What OpenQuestion holds
Registered trials
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.