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Generative Artificial Intelligence: Implications for Biomedical and Health Professions Education.

William Hersh¹

¹Division of Informatics, Clinical Epidemiology and Translational Science, Department of Medicine, School of Medicine, Oregon Health & Science University, Portland, Oregon, USA;

Annual Review of Biomedical Data Science

|April 9, 2025

Summary

This summary is machine-generated.

Generative artificial intelligence (AI), particularly large language models (LLMs), shows promise in health education but requires careful implementation to ensure skill acquisition for professional practice.

Keywords:

artificial intelligence education educational assessment generative artificial intelligence large language models professional

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Area of Science:

Biomedicine and Health
Artificial Intelligence in Education

Background:

Generative AI, powered by large language models (LLMs), is increasingly impacting biomedicine and health sectors.
LLMs demonstrate human-level performance in simulated medical exams, clinical question answering, and case resolution.

Purpose of the Study:

To review the successes and challenges of LLMs in health education.
To provide recommendations for best practices in LLM integration for education.

Main Methods:

Literature review of generative AI applications in biomedicine and health education.
Analysis of LLM performance in academic settings and professional skill development.

Main Results:

LLMs excel in academic assessments and information summarization within health education.
Key challenges include potential negative impacts on knowledge and skill acquisition for professional work.

Conclusions:

Despite challenges, understanding and competence in generative AI are essential for all students and faculty in health and beyond.
Best practices are needed to mitigate risks and maximize the benefits of LLMs in education.