Overview of RAG-based and LLM-based approaches to personalization in healthcare AI applications
Abstract
Recent advances in Large Language Models (LLMs), driven by transformer architectures such as Generative Pre-Trained Transformer (GPT), are opening new frontiers in healthcare Artificial Intelligence (AI) by enabling clinically relevant interactions between patients and clinicians. Yet persistent challenges-including limited real-time knowledge access, safety concerns and insufficient patient-centered contextualization-indicate that current systems often fall short in delivering efficient and reliably personalized responses (Li et al., 2025; Cascella et al., 2023). To address these gaps, Retrieval-Augmented Generation (RAG) can couple LLMs with external knowledge sources at inference time, producing responses that are more grounded, up-to-date, and tailored to individual patient needs. This systematic review examines how personalization is operationalized in LLM-only and RAG-enhanced healthcare AI systems. We searched multiple scholarly sources and included 20 studies in the final qualitative synthesis. We compare personalization strategies, evaluation practices, and trustworthiness challenges, with emphasis on healthcare-specific issues such as privacy, safety, and clinically meaningful context integration. We analyze how current evaluation frameworks assess these systems and identify limitations in their ability to reflect clinically meaningful performance. We further include a brief MedQuAD-based illustrative case study to highlight limitations of current evaluation…
Read the paper
Related studies
- Knowledge Management Perspective of Generative Artificial Intelligence
- Navigating Generative Artificial Intelligence Promises and Perils for Knowledge and Creative Work
- Characterizing the evolving research on enterprise content management
- Large language models and multimodal foundation models for precision oncology
- From Generative AI to Extended Reality: Multidisciplinary Perspectives on the Challenges, Opportunities, and Future of Educational Computing
- Generative Artificial Intelligence: A New Engine for Advancing Environmental Science and Engineering
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.