Generative AI in Improving Personalized Patient Care Plans: Opportunities and Barriers Towards Its Wider Adoption
| aut.relation.endpage | 10899 | |
| aut.relation.issue | 23 | |
| aut.relation.journal | Applied Sciences (Switzerland) | |
| aut.relation.startpage | 10899 | |
| aut.relation.volume | 14 | |
| dc.contributor.author | Baig, MM | |
| dc.contributor.author | Hobson, C | |
| dc.contributor.author | GholamHosseini, H | |
| dc.contributor.author | Ullah, E | |
| dc.contributor.author | Afifi, S | |
| dc.date.accessioned | 2025-02-05T03:13:01Z | |
| dc.date.available | 2025-02-05T03:13:01Z | |
| dc.date.issued | 2024-11-25 | |
| dc.description.abstract | The 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.citation | Applied Sciences (Switzerland), ISSN: 2076-3417 (Print); 2076-3417 (Online), MDPI AG, 14(23), 10899-10899. doi: 10.3390/app142310899 | |
| dc.identifier.doi | 10.3390/app142310899 | |
| dc.identifier.issn | 2076-3417 | |
| dc.identifier.issn | 2076-3417 | |
| dc.identifier.uri | http://hdl.handle.net/10292/18598 | |
| dc.language | en | |
| dc.publisher | MDPI AG | |
| dc.relation.uri | https://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.accessrights | OpenAccess | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | 46 Information and Computing Sciences | |
| dc.subject | 4203 Health Services and Systems | |
| dc.subject | 42 Health Sciences | |
| dc.subject | Networking and Information Technology R&D (NITRD) | |
| dc.subject | Machine Learning and Artificial Intelligence | |
| dc.subject | 8.1 Organisation and delivery of services | |
| dc.subject | 8.3 Policy, ethics, and research governance | |
| dc.subject | Generic health relevance | |
| dc.subject | 3 Good Health and Well Being | |
| dc.title | Generative AI in Improving Personalized Patient Care Plans: Opportunities and Barriers Towards Its Wider Adoption | |
| dc.type | Journal Article | |
| pubs.elements-id | 580792 |
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