Generative AI in Improving Personalized Patient Care Plans: Opportunities and Barriers Towards Its Wider Adoption
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Artificial Intelligence in Healthcare and Education · Ethics in Clinical Research · Ethics and Social Impacts of AI
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Orion Health, Auckland 1010, New Zealand
Orion Health, Toronto, ON M5H 3P5, Canada
School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand
Anatomical Pathology, Department of Pathology and Laboratory Medicine, Te Toka Tumai Auckland, Te Whatu Ora (Health New Zealand), Auckland 1061, New Zealand
Computer Science and Engineering Department, Faculty of Media Engineering & Technology, German University in Cairo, Cairo 11835, Egypt
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