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ChatGPT Applications in Heart Failure: Patient Education, Readability Enhancement, and Clinical Utility

Robert S Doyle1, Jack Hartnett1, Hugo C Temperley2,3

  • 1Cardiology Department, Mater Misericordiae University Hospital, Eccles St., D07 R2WY Dublin, Ireland.

Journal of Cardiovascular Development and Disease
|November 26, 2025
PubMed

Insights

ChatGPT shows promise for improving heart failure (HF) care by enhancing patient education and simplifying complex information. Further research is needed to validate its clinical impact and address potential biases.

Area of Science:

  • Artificial Intelligence in Medicine
  • Digital Health
  • Cardiology

Background:

  • Heart failure (HF) impacts over 64 million globally, causing significant morbidity and mortality.
  • Suboptimal adherence to guideline-directed therapies is linked to low health literacy and complex treatment plans.
  • Large language models like ChatGPT offer potential solutions for HF education and management.

Purpose of the Study:

  • To systematically review the applications of ChatGPT in heart failure care.
  • To evaluate ChatGPT's accuracy in patient education, question-answering, and clinical documentation.
  • To assess ChatGPT's effectiveness in improving health information readability and extracting clinical symptoms.

Main Methods:

  • A systematic review adhering to PRISMA guidelines was conducted.
  • Searches were performed on PubMed, Embase, and Cochrane databases up to July 2025.
  • Included studies focused on ChatGPT (3.5 or 4) in HF contexts; thematic synthesis was applied.

Main Results:

  • Seven observational studies were included, focusing on patient education, readability, and symptom extraction.
  • ChatGPT demonstrated accuracy ranging from 78-98% in patient education and question-answering.
  • Readability was improved to 6th-7th grade levels, and symptom extraction achieved up to 95% F1 score.

Conclusions:

  • ChatGPT shows significant potential for enhancing heart failure patient education and clinical documentation.
  • The technology demonstrated improved readability and accurate symptom extraction.
  • Further randomized controlled trials are necessary to validate clinical outcomes and mitigate potential biases.

Keywords:
ChatGPTartificial intelligenceheart failurelanguage modelspatient educationsystematic review

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