AI evidence extraction

Artificial Intelligence in Medical Education and Assessment: The next step in the IT Revolution

Sami Shaban, Mohi Eldin Magzoub · F1000Research · 2025

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

7/10

Relevance

0/4

Quality (LMQS)

Evidence

Citations

0.89

FWCI

Top 10%

Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.12688/f1000research.173611.1

Methodology & findings

Study design

Narrative literature review synthesizing reviews, empirical investigations, and expert opinions on AI/IT integration in medical education

Main result

The review found that "these tools increase knowledge retention, encourage clinical reasoning, and provide safe environments for skills acquisition" and that "The use of AI applications such as adaptive learning and automated testing helps to develop individualized learning which can be customized to the needs of individual learners."

Research paradigm

interpretivist/argumentative

Author conclusions

The authors conclude that "By providing a synthesis of evidence around currently available technologies, this review offers an understanding of the nature and impact of IT/AI on medical education, which may guide those preparing the next generation of healthcare professionals for an increasingly digital clinical world."

Risk of bias

Selection bias: narrative review methodology without systematic search protocol; Publication bias: unclear inclusion/exclusion criteria for reviewed literature; Confirmation bias: potential bias toward positive findings on AI in education; Selection bias in literature included (narrative review with unclear inclusion criteria); Publication bias (no systematic search protocol documented); Potential author bias toward positive findings on AI in education; Unclear search strategies and databases consulted

Limitations

The authors note that "challenges for the widespread adoption of AI applications exist such as high implementation costs, faculty preparedness, data privacy, learner misuse, algorithm biases and unequal access." The review does not appear to employ systematic search protocols or quantitative meta-analysis, limiting the rigor of evidence synthesis.

Open questions raised

Teaching AI literacy as part of medical curricula
Using AI-driven mixed reality simulations
Developing interdisciplinary collaboration to support responsible AI adoption
Developing standards to support seamless integration between IT and AI systems
Future directions highlighted include: teaching AI literacy as part of medical curricula, using AI-driven mixed reality simulations, developing interdisciplinary collaboration to support responsible AI adoption, and developing standards to support seamless integration between IT and AI systems. Additionally, there is growing appreciation for curriculum changes that incorporate AI literacy and digital skills in undergraduate, graduate, and continuing medical training.
Future directions highlighted include: teaching AI literacy as part of medical curricula, using AI-driven mixed reality simulations, developing interdisciplinary collaboration to support responsible AI adoption, and developing standards to support seamless integration between IT and AI systems.

Data: not_statedCode: not_statedExtracted from: pdfAgreement 81%

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