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Evaluating the Teaching Effectiveness of Interactive Virtual Platform Based on Artificial Intelligence for Obstetrics
Wen Hu1, Zhiming Ding1, Xuezhi Zhao1
1Department of Obstetrics & Gynecology, Women's Hospital, Zhejiang University, School of Medicine, Hangzhou, People's Republic of China.
Objective:
This study aims to create and validate the teaching effectiveness of an interactive artificial intelligence (AI) virtual platform in the standardized residency training of obstetricians and gynecologists.
Methods:
This study randomly selected 70 obstetricians and gynecologists undergoing standardized training at the Obstetrics and Gynecology Hospital affiliated with Zhejiang University School of Medicine. The participants were divided into an experimental group and a control group in a 1:1 ratio. Physicians in the control group directly treated real patients, while those in the experimental group first received virtual case training on an interactive AI virtual platform based on the DeepSeek V3 large language model before treating real patients.
Results:
The total score for the six core competencies (clinical practice ability, medical humanities literacy, critical thinking, teamwork ability, information integration ability, and lifelong learning ability) of the resident physicians in the experimental group was significantly higher than that of the control group (89.14 ± 3.919 vs 82.49 ± 5.078, P < 0.001). Especially in terms of clinical practice ability, critical thinking, information integration ability, and lifelong learning ability, the experimental group showed outstanding performance. In addition, the satisfaction of the experimental group physicians with teaching effectiveness, clinical ability improvement, and decision-making confidence improvement was significantly higher than that of the control group (P < 0.05). The results of the patient satisfaction survey showed that the evaluation of the experimental group physicians in terms of medical ability, communication ability, and medical ethics was also better than that of the control group (P < 0.05).
Conclusion:
The addition of AI-based virtual case training to standard teaching-clinic practice in obstetrics and gynecology education is an effective innovative approach. This platform not only enriches teaching resources and enhances the core competencies of resident physicians but also significantly improves teaching satisfaction and patient satisfaction.