What is it about?
This study contributes to the growing body of literature on the application of AI in healthcare by specifically examining the efficacy of AI language models in translating complex radiology reports into patient-friendly language. Previous research has explored the capabilities of AI in various medical contexts, such as cardiology and oncology, focusing on the readability and comprehension of medical information (Hillmann et al., Seth et al.). However, this study uniquely addresses the triage potential of AI language models in radiology, highlighting their ability to not only simplify technical reports but also assess the urgency of medical conditions.
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Why is it important?
By evaluating models like ChatGPT-4, BARD, and Microsoft Copilot, this research provides valuable insights into their comparative performance in enhancing patient understanding and guiding follow-up actions, thus bridging a crucial gap in patient-provider communication.
Read the Original
This page is a summary of: Decoding medical jargon: The use of AI language models (ChatGPT-4, BARD, microsoft copilot) in radiology reports, Patient Education and Counseling, September 2024, Elsevier,
DOI: 10.1016/j.pec.2024.108307.
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