Accuracy, satisfaction, and impact of custom GPT in acquiring clinical knowledge: Potential for AI-assisted medical education

Abstract

BACKGROUND: Recent advancements in artificial intelligence (AI) have enabled the customization of large language models to address specific domains such as medical education. This study investigates the practical performance of a custom GPT model in enhancing clinical knowledge acquisition for medical students and physicians. METHODS: A custom GPT was developed by incorporating the latest readily available teaching resources. Its accuracy in providing clinical knowledge was evaluated using a set of clinical questions, and responses were compared against established medical guidelines. Satisfaction was assessed through surveys involving medical students and physicians at different stages and from various types of hospitals. The impact of the custom GPT was further evaluated by comparing its role in facilitating clinical knowledge acquisition with traditional learning methods. RESULTS: < 0.05), though fewer perfect scores were obtained. CONCLUSIONS: The custom GPT demonstrates significant promise as an innovative tool for advancing medical education, particularly for residents. Its capability to deliver accurate, tailored information complements traditional teaching methods, aiding educators in promoting personalized and consistent training. However, it is essential for both learners and educators to remain critical in evaluating AI-generated information. With continued development and thoughtful integration, AI tools like custom GPTs have the potential to significantly enhance the quality and accessibility of medical education.

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