The Effectiveness of Large Language Models in Providing Automated Feedback in Medical Imaging Education: A Protocol for a Systematic Review
Authors
Affiliations (1)
Affiliations (1)
- Institute of Learning (IoL), Mohammed Bin Rashid University of Medicine and Health Sciences (MBRU), Dubai Health, Dubai, United Arab Emirates.
Abstract
BackgroundLarge Language Models (LLMs) represent an ever-emerging and rapidly evolving generative artificial intelligence (AI) modality with promising developments in the field of medical education. LLMs can provide automated feedback services to medical trainees (i.e. medical students, residents, fellows, etc.) and possibly serve a role in medical imaging education. AimThis systematic review aims to comprehensively explore the current applications and educational outcomes of LLMs in providing automated feedback on medical imaging reports. MethodsThis study employs a comprehensive systematic review strategy, involving an extensive search of the literature (Pubmed, Scopus, Embase, and Cochrane), data extraction, and synthesis of the data. ConclusionThis systematic review will highlight the best practices of LLM use in automated feedback of medical imaging reports and guide further development of these models.