Evidence map›Paper›PMID 40620453›Full record

ArticleWorld journal of clinical cases2025

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

Rishith Mishra, Hersh Patel, Aleena Jamal, Som Singh

Abstract readEditorial
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Rishith MishraSchool of Medicine, University of Missouri Kansas City, Kansas City, MO 64106, United States.
Hersh PatelSchool of Medicine, University of Missouri Kansas City, Kansas City, MO 64106, United States.
Aleena JamalSidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA 19107, United States.
Som SinghSchool of Medicine, University of Missouri Kansas City, Kansas City, MO 64106, United States. somsingh@mail.umkc.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Artificial intelligenceCardiac rehabilitationLarge language modelsMotivational interviewingPatient education

Identifiers

PMID40620453
PMCPMC11926480

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.