Rui Li,1 Tong Wu2– 4 

1Emergency Department, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China; 2National Clinical Research Center for Obstetrical and Gynecological Diseases, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China; 3Key Laboratory of Cancer Invasion and Metastasis, Ministry of Education, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China; 4Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China

Correspondence: Tong Wu, Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095, Jiefang Avenue, Wuhan, 430030, People’s Republic of China, Email tongwu66@tjh.tjmu.edu.cn

Abstract: As the rapid development of large language model, artificial intelligence generated content (AIGC) presents novel opportunities for constructing medical examination questions. However, it is unclear about the way of effectively utilizing AIGC for designing medical questions. AIGC is characterized by its rapid response capabilities and high efficiency, as well as good performance in mimicking clinical realities. In this study, we revealed the limitations inherent in paper-based examinations, and provided a streamlined instruction for generating questions using AIGC, with a particular focus on multiple-choice questions, case study questions, and video questions. Manual review remains necessary to ensure the accuracy and quality of the generated content. Future development will be benefited from technologies like retrieval augmented generation, multi-agent system, and video generation technology. As AIGC continues to evolve, it is anticipated to bring transformative changes to medical examinations, enhancing the quality of examination preparation, and contributing to the effective cultivation of medical students.

Keywords: artificial intelligence generated content, medical education, multiple-choice question, large language model