article · Applied Sciences
Generative artificial intelligence offers significant potential to improve personalised patient care plans, but successful adoption requires addressing substantial technical, operational, and ethical barriers. A systematic review of thirteen studies identified that responsible implementation demands a holistic strategy connecting artificial intelligence developers, healthcare professionals, regulatory authorities, and patients. Key technical challenges include ensuring model explainability, thorough clinical validation, patient privacy protection, and seamless integration into existing clinical workflows. Furthermore, creating transparent frameworks that focus on medical data governance and ethical standards is essential. Achieving the balance between harnessing generative models for patient care and mitigating risks relies heavily on cross-sector collaboration. By establishing clear standards and robust protocols, healthcare systems can prepare to integrate generative artificial intelligence into routine personalised care whilst safeguarding patient trust and clinical safety.
Personalised care plans tailor treatments to individual patient needs, leading to better health outcomes. Generative artificial intelligence could assist medical teams in building these plans more effectively. Understanding the operational, ethical, and regulatory hurdles ensures that advanced healthcare technologies are introduced safely, preserving patient privacy and trust while supporting clinical staff.
The findings address software developers, data governance bodies, and healthcare organisations seeking to integrate generative artificial intelligence into clinical planning tools. Because the evidence highlights unresolved barriers around workflow integration, model explainability, and regulatory compliance, the application appears to be at an early stage of clinical adoption. Commercial progress will require validated frameworks that satisfy stringent medical privacy and safety requirements.
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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.
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DOI: 10.3390/app142310899
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