In JMIR medical education
BACKGROUND : The use of artificial intelligence (AI) in medicine will generate numerous application possibilities to improve patient care, provide real-time data analytics, and enable continuous patient monitoring. Clinicians and health informaticians should become familiar with machine learning and deep learning. Additionally, they should have a strong background in data analytics and data visualization to use, evaluate, and develop AI applications in clinical practice.
OBJECTIVE : The main objective of this study was to evaluate the current state of AI training and the use of AI tools to enhance the learning experience.
METHODS : A comprehensive systematic review was conducted to analyze the use of AI in medical and health informatics education, and to evaluate existing AI training practices. PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols) guidelines were followed. The studies that focused on the use of AI tools to enhance medical education and the studies that investigated teaching AI as a new competency were categorized separately to evaluate recent developments.
RESULTS : This systematic review revealed that recent publications recommend the integration of AI training into medical and health informatics curricula.
CONCLUSIONS : To the best of our knowledge, this is the first systematic review exploring the current state of AI education in both medicine and health informatics. Since AI curricula have not been standardized and competencies have not been determined, a framework for specialized AI training in medical and health informatics education is proposed.
Sapci A Hasan, Sapci H Aylin
artificial intelligence, deep learning, education, health informatics, machine learning, medical education, systematic review