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dc.contributor.authorBarreda Gutiérrez, Marina 
dc.contributor.authorCantarero Prieto, David 
dc.contributor.authorCoca, Daniel
dc.contributor.authorDelgado Diego, Abraham
dc.contributor.authorLanza León, Paloma 
dc.contributor.authorLera Torres, Javier Isaac 
dc.contributor.authorMontalbán Carrasco, María del Rocío
dc.contributor.authorPérez Hernández, Flora
dc.contributor.otherUniversidad de Cantabriaes_ES
dc.date.accessioned2025-04-02T07:59:09Z
dc.date.available2025-04-02T07:59:09Z
dc.date.issued2025
dc.identifier.issn2055-2076
dc.identifier.urihttps://hdl.handle.net/10902/36162
dc.description.abstractPurpose: The COVID-19 pandemic has intensified the demand and use of healthcare resources, prompting the search for efficient solutions under budgetary constraints. In this context, the increasing use of artificial intelligence and telemedicine has emerged as a key strategy to optimize healthcare delivery and resources. Consequently, chatbots have emerged as innovative tools in various healthcare fields, such as mental health and patient monitoring, offering therapeutic conversations and early interventions. This systematic review aims to explore the current state of chatbots in the healthcare sector, meticulously evaluating their effectiveness, practical applications, and potential benefits. Methods: This systematic review was conducted following PRISMA guidelines, utilizing three databases, including PubMed, Web of Science, and Scopus, to identify relevant studies on the use and cost of chatbots in health over the past 5 years. Results: Several articles were identified through the database search (n=31). The chatbot interventions were categorized by similar types. The reviewed articles highlight the diverse applications of chatbot interventions in healthcare, including mental health support, medical information, appointment management, health education, lifestyle changes, and COVID-19 management, demonstrating significant potential across these areas. Conclusion: Furthermore, there are challenges regarding the implementation of chatbots, compatibility with other systems, and ethical considerations that may arise in different healthcare settings. Addressing these issues will be essential to maximize the benefits of chatbots, mitigate risks, and ensure equitable access to these health innovations.es_ES
dc.description.sponsorshipThe authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the Government of Cantabria (Grant Number: SUBVTC-2023-0021). This study is also funded by the European Commission in the Horizon H2020 scheme, awarded to the TIMELY project (Grant agreement ID: 101017424).es_ES
dc.format.extent22 p.es_ES
dc.language.isoenges_ES
dc.publisherSage Publications Ltd.es_ES
dc.rights© 2025. The Author(s). Reprinted by permission of SAGE Publications. Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).es_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.sourceDigital Health, 2025, 11, 1-22es_ES
dc.subject.otherArtificial intelligencees_ES
dc.subject.otherChatbotses_ES
dc.subject.otherHealthcarees_ES
dc.subject.otherCostes_ES
dc.subject.otherPatient engagementes_ES
dc.titleTransforming healthcare with chatbots: uses and applications. A scoping reviewes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherVersionhttps://doi.org/10.1177/20552076251319174es_ES
dc.rights.accessRightsopenAccesses_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/101017424/EU/A patient-centered early risk prediction, prevention, and intervention platform to support the continuum of care in coronary artery disease (CAD) using eHealth and artificial intelligence/TIMELY/es_ES
dc.identifier.DOI10.1177/20552076251319174
dc.type.versionpublishedVersiones_ES


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© 2025. The Author(s). Reprinted by permission of SAGE Publications. Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).Excepto si se señala otra cosa, la licencia del ítem se describe como © 2025. The Author(s). Reprinted by permission of SAGE Publications. Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).