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TRUVACE RECORD VERSION
record: TRV-2026-2784
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:23:27.799855Z
status: published
lens: p_space
sector: health
headline: Is it time to introduce an artificial intelligence curriculum in undergraduate medical education? medical students’ perspectives: a systematic review and meta-analysis
dek: BACKGROUND: Although artificial intelligence (AI) is increasingly transforming healthcare systems, its systematic integration into undergraduate medical education (UME) remains limited. Despite widespread recognition of AI's potential to enhance clinical practice, AI-related competencies are still inadequately embedded in most medical curricula, even though medical students generally express positive attitudes toward AI. OBJECTIVES: This systematic review and meta-analysis synthesizes the current literature on m…
gain_title: (none)
problem_title: A systematic review of 26 studies of 20,963 medical students found 83.1% agreed AI training is necessary, yet only 36.4% felt confident applying AI in clinical practice.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: A systematic review of 26 studies of 20,963 medical students found 83.1% agreed AI training is necessary, yet only 36.4% felt confident applying AI in clinical practice.
problem_evidence: only 7,630 participants (36.4%) felt confident in applying AI in clinical practice | 17,420 participants (83.1%) agreed on the necessity for AI training
quick_read: A systematic review and meta-analysis pooled 26 studies covering 20,963 medical students. It found 78.0% (16,278) held positive attitudes toward adding AI to the undergraduate curriculum, and 83.1% (17,420) agreed AI training is necessary.

Only 36.4% (7,630) of students felt confident applying AI in clinical practice. The authors call this the 'optimism-competence gap' and say a standardized, competency-based AI curriculum is needed to close it.

Schools face barriers to teaching AI. Curricular overcrowding was reported in 13 of 19 studies (68%), lack of faculty expertise in 12 of 23 (52%) and ethical concerns in 9 of 22 (41%). Support for AI teaching was consistent across geographic regions.
limitation: The meta-analysis found extreme heterogeneity across studies (I² = 98.5%), so the pooled proportions mask large differences between the underlying studies.
tag: Evidence-backed problem
key_points: The review searched PubMed, Web of Science, Scopus and Cochrane Library for studies published between January 2000 and February 2025, following PRISMA guidelines. | It included 26 studies with 20,963 medical students in total. | In the pooled analysis, 78.0% of students (16,278) held positive attitudes toward AI curriculum integration. | 83.1% of students (17,420) agreed AI training is necessary, but only 36.4% (7,630) felt confident using AI clinically. | Curricular overcrowding was the most common barrier, reported in 68% of the studies that assessed it, ahead of faculty expertise gaps (52%) and ethical concerns (41%). | The authors recommend prioritizing faculty training, interdisciplinary collaboration and ethical frameworks.
rundown: The authors searched four databases for studies from January 2000 to February 2025 and pooled student support using a random-effects model with Freeman-Tukey transformation. The 26 included studies assessed attitudes and challenges related to learning AI among 20,963 medical students.

Students were broadly supportive: 78.0% favored integrating AI into the curriculum, and subgroup analyses showed the support held across geographic regions. Confidence lagged far behind, with 36.4% feeling able to apply AI in clinical practice.

On the supply side, the barriers were crowded curricula, limited faculty expertise and ethical concerns. The authors conclude that a standardized AI curriculum is urgently needed and that faculty training should come first.
sources:
- peer_reviewed | BMC Medical Education | https://doi.org/10.1186/s12909-026-09630-9 | 2026-06-17
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