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

Abstract

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.

Keywords: Cardiac rehabilitation; Large language models; Patient education; Motivational interviewing; Artificial intelligence

Core Tip: Large language models may help innovate cardiac rehabilitation programs on a larger scale, but there is a large paucity in evidence to support its utility and evaluating the validity of these innovative proposals. Likewise, this new innovation may be able to assist in developing more personalized medicine for patients and clinical research.