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Applications of large language models in cardiovascular disease: a systematic review

2025-07-01Review article Open access
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dc.contributor.authorSantos, José Ferreira
dc.contributor.authorLadeiras-Lopes, Ricardo
dc.contributor.authorLeite, Francisca
dc.contributor.authorDores, Hélder
dc.date.accessioned2025-08-05T07:18:40Z
dc.date.available2025-08-05T07:18:40Z
dc.date.issued2025-07-01
dc.description.abstractCardiovascular disease (CVD) remains the leading cause of morbidity and mortality worldwide. Large language models (LLMs) offer potential solutions for enhancing patient education and supporting clinical decision-making. This study aimed to evaluate LLMs’ applications in CVD and explore their current implementation, from prevention to treatment. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, this systematic review assessed LLM applications in CVD. A comprehensive PubMed search identified relevant studies. The review prioritized pragmatic and practical applications of LLMs. Key applications, benefits, and limitations of LLMs in CVD prevention were summarized. Thirty-five observational studies met the eligibility criteria. Of these, 54% addressed primary prevention and risk factor management, while 46% focused on established CVD. Commercial LLMs were evaluated in all but one study, with 91% (32 studies) assessing ChatGPT. The LLM applications were categorized as follows: 72% addressed patient education, 17% clinical decision support, and 11% both. In 68% of studies, the primary objective was to evaluate LLMs’ performance in answering frequently asked patient questions, with results indicating accurate, comprehensive, and generally safe responses. However, occasional misinformation and hallucinated references were noted. Additional applications included patient guidance on CVD, first aid, and lifestyle recommendations. Large language models were assessed for medical questions, diagnostic support, and treatment recommendations in clinical decision support. Large language models hold significant potential in CVD prevention and treatment. Evidence supports their potential as an alternative source of information for addressing patients’ questions about common CVD. However, further validation is needed for their application in individualized care, from diagnosis to treatment.eng
dc.identifier.doi10.1093/ehjdh/ztaf028
dc.identifier.eid105011510324
dc.identifier.otheree0489e0-4a7d-4f6b-956a-4a54ff0d7784
dc.identifier.pmcPMC12282349
dc.identifier.pmid40703130
dc.identifier.urihttp://hdl.handle.net/10400.14/54132
dc.language.isoeng
dc.peerreviewedyes
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectArtificial intelligence
dc.subjectCardiovascular disease
dc.subjectClinical decision
dc.subjectLarge language models (LLMs)
dc.subjectPatient education
dc.subjectPrevention
dc.titleApplications of large language models in cardiovascular disease: a systematic revieweng
dc.typereview article
dspace.entity.typePublication
oaire.citation.endPage553
oaire.citation.issue4
oaire.citation.startPage540
oaire.citation.titleEuropean Heart Journal - Digital Health
oaire.citation.volume6
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85

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