ArticleWorld journal of clinical cases2025
Potential role of large language models and personalized medicine to innovate cardiac rehabilitation.
Article in World journal of clinical cases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
4 authors.
Funding
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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.
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