作者
Yaara Artsi, Vera Sorin, Eli Konen, Benjamin S Glicksberg, Girish Nadkarni, Eyal Klang
发表日期
2024/3/29
来源
BMC Medical Education
卷号
24
期号
1
页码范围
354
出版商
BioMed Central
简介
Background
Writing multiple choice questions (MCQs) for the purpose of medical exams is challenging. It requires extensive medical knowledge, time and effort from medical educators. This systematic review focuses on the application of large language models (LLMs) in generating medical MCQs.
Methods
The authors searched for studies published up to November 2023. Search terms focused on LLMs generated MCQs for medical examinations. Non-English, out of year range and studies not focusing on AI generated multiple-choice questions were excluded. MEDLINE was used as a search database. Risk of bias was evaluated using a tailored QUADAS-2 tool.
Results
Overall, eight studies published between April 2023 and October 2023 were included. Six studies used Chat-GPT 3.5, while two employed GPT 4. Five studies showed that LLMs can produce competent questions valid for medical exams. Three …
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Y Artsi, V Sorin, E Konen, BS Glicksberg, G Nadkarni… - BMC Medical Education, 2024