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Applications of large language models in cardiovascular disease

2025-07-01Recensão Acesso aberto
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dc.contributor.authorSantos, José Ferreira
dc.contributor.authorLadeiras-Lopes, Ricardo
dc.contributor.authorLeite, Francisca
dc.contributor.authorDores, Hélder
dc.contributor.institutionNOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)
dc.contributor.institutionComprehensive Health Research Centre (CHRC) - pólo NMS
dc.contributor.pblOxford University Press
dc.date.accessioned2025-11-20T21:11:20Z
dc.date.available2025-11-20T21:11:20Z
dc.date.issued2025-07-01
dc.descriptionPublisher Copyright: © The Author(s) 2025.
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.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent14
dc.format.extent1070584
dc.identifier.doi10.1093/ehjdh/ztaf028
dc.identifier.issn2634-3916
dc.identifier.otherPURE: 135962849
dc.identifier.otherPURE UUID: 6b568994-2d12-4160-b739-c78de8084358
dc.identifier.otherScopus: 105011510324
dc.identifier.otherWOS: 001471913800001
dc.identifier.urihttp://hdl.handle.net/10362/191156
dc.identifier.urlhttps://www.scopus.com/pages/publications/105011510324
dc.language.isoeng
dc.peerreviewedyes
dc.subjectArtificial intelligence
dc.subjectCardiovascular disease
dc.subjectClinical decision
dc.subjectLarge language models (LLMs)
dc.subjectPatient education
dc.subjectPrevention
dc.subjectCardiology and Cardiovascular Medicine
dc.subjectSDG 3 - Good Health and Well-being
dc.titleApplications of large language models in cardiovascular diseaseen
dc.title.subtitlea systematic reviewen
dc.typereview
degois.publication.firstPage540
degois.publication.issue4
degois.publication.lastPage553
degois.publication.titleEuropean Heart Journal - Digital Health
degois.publication.volume6
dspace.entity.typePublication
rcaap.rightsopenAccess

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