Potential role of large language models and personalized medicine to innovate cardiac rehabilitation  

作　　者：Rishith Mishra Hersh Patel Aleena Jamal Som Singh 

机构地区：[1]School of Medicine,University of Missouri Kansas City,Kansas City,MO 64106,United States [2]Sidney Kimmel Medical College,Thomas Jefferson University,Philadelphia,PA 19107,United States

出　　处：《World Journal of Clinical Cases》2025年第19期1-4,共4页世界临床病例杂志(英文)

摘　　要：Cardiac rehabilitation is a crucial multidisciplinary approach to improve patient outcomes.There is a growing body of evidence that suggests that these programs contribute towards reducing cardiovascular mortality and recurrence.Despite this,cardiac rehabilitation is underutilized and adherence to these programs has been a demonstrated barrier in achieving these outcomes.As a result,there is a growing focus on innovating these programs,especially from the standpoint of digital health and personalized medicine.This editorial discusses the possible roles of large language models,such as their role in ChatGPT,in further personalizing cardiac rehabilitation programs through simplifying medical jargon and employing motivational interviewing techniques,thus boosting patient engagement and adherence.However,these possibilities must be further investigated in the clinical literature.Likewise,the integration of large language models in cardiac rehabilitation will be challenging in its nascent stages to ensure accurate and ethical information delivery.

关 键 词：Cardiac rehabilitation Large language models Patient education Motivational interviewing Artificial intelligence 

分 类 号：R54[医药卫生—心血管疾病]