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Generative AI in Improving Personalized Patient Care Plans: Opportunities and Barriers Towards Its Wider Adoption

aut.relation.endpage10899
aut.relation.issue23
aut.relation.journalApplied Sciences (Switzerland)
aut.relation.startpage10899
aut.relation.volume14
dc.contributor.authorBaig, MM
dc.contributor.authorHobson, C
dc.contributor.authorGholamHosseini, H
dc.contributor.authorUllah, E
dc.contributor.authorAfifi, S
dc.date.accessioned2025-02-05T03:13:01Z
dc.date.available2025-02-05T03:13:01Z
dc.date.issued2024-11-25
dc.description.abstractThe main aim of this study is to investigate the opportunities, challenges, and barriers in implementing generative artificial intelligence (Gen AI) in personalized patient care plans (PPCPs). This systematic review paper provides a comprehensive analysis of the current state, potential applications, and opportunities of Gen AI in patient care settings. This review aims to serve as a key resource for various stakeholders such as researchers, medical professionals, and data governance. We adopted the PRISMA review methodology and screened a total of 247 articles. After considering the eligibility and selection criteria, we selected 13 articles published between 2021 and 2024 (inclusive). The selection criteria were based on the inclusion of studies that report on the opportunities and challenges in improving PPCPs using Gen AI. We found that a holistic approach is required involving strategy, communications, integrations, and collaboration between AI developers, healthcare professionals, regulatory bodies, and patients. Developing frameworks that prioritize ethical considerations, patient privacy, and model transparency is crucial for the responsible deployment of Gen AI in healthcare. Balancing these opportunities and challenges requires collaboration between wider stakeholders to create a robust framework that maximizes the benefits of Gen AI in healthcare while addressing the key challenges and barriers such as explainability of the models, validation, regulation, and privacy integration with the existing clinical workflows.
dc.identifier.citationApplied Sciences (Switzerland), ISSN: 2076-3417 (Print); 2076-3417 (Online), MDPI AG, 14(23), 10899-10899. doi: 10.3390/app142310899
dc.identifier.doi10.3390/app142310899
dc.identifier.issn2076-3417
dc.identifier.issn2076-3417
dc.identifier.urihttp://hdl.handle.net/10292/18598
dc.languageen
dc.publisherMDPI AG
dc.relation.urihttps://www.mdpi.com/2076-3417/14/23/10899
dc.rights© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
dc.rights.accessrightsOpenAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject46 Information and Computing Sciences
dc.subject4203 Health Services and Systems
dc.subject42 Health Sciences
dc.subjectNetworking and Information Technology R&D (NITRD)
dc.subjectMachine Learning and Artificial Intelligence
dc.subject8.1 Organisation and delivery of services
dc.subject8.3 Policy, ethics, and research governance
dc.subjectGeneric health relevance
dc.subject3 Good Health and Well Being
dc.titleGenerative AI in Improving Personalized Patient Care Plans: Opportunities and Barriers Towards Its Wider Adoption
dc.typeJournal Article
pubs.elements-id580792

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