ReviewBMC medical education2025
Reimagining healthcare education through nurturing AI-driven innovation.
Review 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 2 papers, 2 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
2 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Development of AI competencies within the medical curriculum.Frontiers in medicine · 2026Pooled it
- Comparison of artificial intelligence assisted training and traditional learning paths in clinical simulation skills training: meta-analysis of randomized controlled trials.Frontiers in medicine · 2026Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
purposeThis article explores the transformative role of artificial intelligence (AI) in healthcare medical education, highlighting the urgent need to integrate AI into medical curricula. It examines the current gaps in AI literacy among healthcare professionals, proposes practical teaching strategies, and discusses the ethical considerations essential for the responsible implementation of AI.
methodsAn extensive literature review was conducted to identify key challenges and opportunities in integrating AI into healthcare education. Case studies, existing curricula, and frameworks were analysed to explore effective teaching methodologies, including problem-based learning (PBL), simulations, and ethical embedding. Multidisciplinary collaboration among academic institutions, technology companies, and government organisations was also examined as a pathway to bridge the gap between AI innovation and practical application.
resultsThe findings reveal that while AI is rapidly becoming an integral part of healthcare practice, current medical curricula do not adequately prepare students for its adoption. Integrating AI into curricula through PBL, simulations, and real-world case studies fosters critical thinking and practical skills. Embedding ethical considerations throughout the curriculum addresses data privacy, bias, and accountability issues. Interdisciplinary partnerships and continuous professional development are key strategies for enhancing AI education and ensuring alignment with evolving healthcare needs.
conclusionReimagining healthcare education to incorporate AI is essential for preparing healthcare professionals to navigate an AI-driven future. Embedding AI into curricula, addressing ethical concerns, and fostering collaborations between academia, industry, and government can responsibly equip healthcare professionals to harness AI's potential. This paradigm shift is critical for improving patient care, advancing medical innovation, and ensuring the ethical integration of AI in healthcare systems.
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.