Medical Students' AI Knowledge, Attitudes, and Practice: A Global Systematic Review

A global systematic review and meta-analysis synthesized 43 studies on undergraduate medical students' engagement with artificial intelligence. The study, authored by Paolo Visci and last updated in June 2026, reports pooled estimates showing moderate knowledge, positive attitudes, and comparable practice levels based on a quantitative synthesis of 11 studies.

Use Cases

• Benchmark AI literacy levels among medical students based on the reported knowledge, attitude, and practice scores.
• Inform the design of undergraduate AI curricula based on identified gaps in knowledge and practical engagement.
• Analyze global trends in medical student perceptions of AI using the synthesized data from 43 studies.
• Validate assessment tools for AI readiness in medical education using the described MAIRS-MS scale.

Strengths

• The meta-analysis provides pooled quantitative estimates (e.g., knowledge: 0.55; attitudes: 0.64) from 11 studies.
• Methodology is clearly described, following PRISMA 2020 guidelines and using the AXIS tool for quality assessment.
• The dataset aggregates findings from 43 included studies, providing a broad evidence base.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Data may reflect publication or geographic bias inherent to the systematic review's source selection.

Provenance

Source

figshare

Collection Method

Systematic review and meta-analysis of literature from PubMed, Scopus, and Web of Science up to September 30, 2025.

Time Range

Studies included up to September 2025.

Freshness

Last updated 2026-06-01 03:31:52; freshness should be verified.

Geography

Global (studies from multiple countries).