Utilization of AI among Medical Students and Development of AI Education Platforms in Medical Institutions: A Cross-Sectional Study (Preprint)

BACKGROUND Background: The emergence of Artificial Intelligence (AI) is driving digital transformation and reshaping medical education in China. Numerous medical schools and institutions are actively implementing AI tools for case-based learning, literature analysis, and lecture support. This expanding application is concurrently fostering the adoption of localized AI platforms. Consequently, such platforms are positioned to become a dominant paradigm in the coming years. OBJECTIVE Objectives: The primary aim of this study was to investigate the current use of AI tools among medical students, including usage frequency, commonly used platforms, and purposes of use. The second aim was to explore students’ needs and expectations toward AI-powered medical education platforms by collecting and assessing student feedback, and to identify practical requirements across disciplines and academic stages to inform more effective platform design. METHODS Methods: An anonymous online questionnaire was administered to assess AI usage in learning, student feedback on AI-powered medical education platforms, and expected functionalities. The survey was conducted from March 1 to May 31, 2025, using convenience sampling to target medical students from disciplines including clinical medicine, basic medicine, nursing, rehabilitation therapy, traditional Chinese medicine, public health, epidemiology, pharmacy, and medical engineering across Shanghai, China. RESULTS Results: A total of 428 valid questionnaires were collected. The average frequency of AI-assisted learning among medical students was (5.06±0.10) times per week. Over 90% of students (388/428) used more than two AI tools in their daily tasks. The average satisfaction score with the AI education platforms at their schools was (72.23±21.84), with significant individual differences. Students from different disciplines, educational stages, and academic systems demonstrated different usage patterns and expectations for AI-powered medical education platforms. CONCLUSIONS Conclusions: AI technology is widely accepted by medical students and is extensively applied across various aspects of medical education. Significant differences are observed in usage patterns across disciplines, educational stages, and academic systems. Understanding the actual needs of students is crucial for the construction of AI-powered medical education platforms.