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Large language models for generating medical examinations: systematic review.
Yaara Artsi1, Vera Sorin2,3,4, Eli Konen2,3
1Azrieli Faculty of Medicine, Bar-Ilan University, Ha'Hadas St. 1, Rishon Le Zion, Zefat, 7550598, Israel. yaara.artsi77@gmail.com.
BMC Medical Education
|March 30, 2024
Summary
Large language models (LLMs) show potential for generating medical multiple-choice questions (MCQs). However, current AI-generated MCQs require significant revision for exam validity, suggesting LLMs are best used as supplementary tools.
Area of Science:
- Medical Education Technology
- Artificial Intelligence in Healthcare
- Assessment and Evaluation
Background:
- Medical educators face challenges in creating high-quality multiple-choice questions (MCQs) for exams.
- This systematic review examines the utility of large language models (LLMs) in generating medical MCQs.
Conclusions:
- LLMs show promise for assisting in the creation of medical MCQs.
- Current LLM-generated MCQs necessitate careful review and modification for exam suitability.
- Further research is required to establish conclusive evidence; LLMs are currently best viewed as supplementary tools.
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