In 1956, the concept of artificial intelligence (AI) was introduced by John McCarthy during the Dartmouth conference, marking the inception of a field that would revolutionize various industries. In recent years, AI, particularly in the medical sector, has witnessed significant advancements, with systems capable of diagnosing diseases with expert-level accuracy. This has transformed healthcare services, improving patient outcomes and promoting overall well-being.Â
Despite these advancements, medical education has been slow to integrate AI breakthroughs into its curriculum. A recent study investigated the attitudes of medical students in China towards medical AI, utilizing the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework. This model, commonly applied in diverse contexts, aims to identify factors influencing the acceptance of technology.Â
The study found that while medical AI has made considerable strides, approximately 45.7% of the surveyed medical students were not familiar with the field. Moreover, only a limited number of medical schools in China have incorporated AI topics into their curriculum, highlighting a significant gap in medical education.Â
The primary drivers influencing the acceptance and utilization of medical AI among students were identified as performance expectancy, hedonic motivation, habit, and trust. Performance expectancy refers to the belief that using a specific system will improve task performance, while hedonic motivation relates to the pleasure derived from utilizing a system. Habit signifies automatic or habitual behavior, and trust is a critical factor in acceptance and intention to use medical AI.Â
The study proposes key recommendations for the future of medical AI education. Firstly, efforts should be directed towards enhancing awareness among students unfamiliar with medical AI. Secondly, collaborative initiatives between academia and industry should be pursued to integrate AI topics into the medical curriculum.
Thirdly, there is a need for targeted training that addresses students’ performance expectancy, hedonic motivation, habit formation, and trust. For the future design of medical AI, the study suggests emphasizing the enhancement of performance, ensuring user-friendly and engaging experiences, and establishing trust. These recommendations aim to prepare medical students for the evolving landscape of healthcare, where AI is expected to play an increasingly vital role.Â
However, the study acknowledges its limitations, including potential bias in the online survey method and voluntary participation. The findings, collected from 33 hospitals and 11 universities in seven Chinese provinces, may not be fully representative of the entire country. Additionally, the lack of systematic education on medical AI for medical undergraduates and postgraduates could influence their perspectives.Â
In conclusion, as medical AI continues to shape the future of healthcare, it is imperative to bridge the gap between technological advancements and medical education. Integrating AI into curricula, raising awareness, and addressing students’ needs are essential steps to ensure that future healthcare professionals are well-prepared to leverage the benefits of medical AI in their practice. Â
Journal Reference Â
Li, Q., & Qin, Y. (2023). BMC Medical Education, 23(1). doi:10.1186/s12909-023-04700-8Â
