AI Use for Medical Students: Impact on Clinical Skill Acquisition and Retention. A Systematic Review

Area of Science:

• Medical Education Technology
• Artificial Intelligence in Healthcare
• Clinical Reasoning Development

Background:

• Artificial Intelligence (AI) integration in undergraduate medical education is growing.
• Concerns exist regarding AI's potential negative impact on clinical reasoning skills.
• Deskilling and upskilling inhibition are potential risks associated with AI automation in learning.

Purpose of the Study:

• To systematically review the effects of AI-supported learning on clinical skill acquisition and retention in medical students.
• To assess the potential negative impact of AI on clinical reasoning development in medical education.

Main Methods:

• Systematic search of PubMed, Scopus, and Embase conducted on October 21, 2025.
• Inclusion criteria focused on studies involving medical students, AI tools in clinical learning, and outcomes related to skill acquisition or reasoning.
• PRISMA guidelines were followed for study screening and selection.

Main Results:

• Four studies involving 408 medical students were included.
• AI exposure correlated with improved efficiency and basic knowledge acquisition.
• Mixed results were observed for higher-order clinical reasoning, with some studies indicating weaker performance or reduced engagement with AI-supported methods.

Conclusions:

• AI-supported learning shows promise for efficiency and basic knowledge but has mixed effects on higher-order clinical reasoning.
• The limited evidence base highlights potential risks of deskilling and upskilling inhibition.
• Further research is crucial to understand AI's long-term impact on clinical judgment and to develop strategies mitigating overreliance.