Large Language Models in Healthcare

2025-02-06

AI Summary

This study addresses critical challenges hindering the clinical deployment of large language models (LLMs): patient privacy, algorithmic bias, regulatory compliance, and operational sustainability. Methodologically, we propose the first healthcare-specific, four-dimensional adaptation framework comprising: (1) domain-adaptive fine-tuning, (2) clinically informed prompt engineering, (3) multimodal electronic health record (EHR) integration—unifying unstructured text and structured data—and (4) a novel evaluation paradigm centered on clinical accuracy, fairness, robustness, and outcome-oriented metrics. Crucially, privacy-preserving mechanisms, bias mitigation strategies, and regulatory requirements (e.g., HIPAA, FDA guidelines) are systematically embedded throughout the technical design lifecycle. The work yields a reproducible implementation roadmap with clearly defined interdisciplinary collaboration protocols. It provides both theoretical foundations and actionable guidance for the safe, effective, and compliant integration of LLMs into clinical decision support, patient-facing applications, and healthcare administrative automation.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Humans and AI: AI for Accessibility

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

Large language models (LLMs) hold promise for transforming healthcare, from streamlining administrative and clinical workflows to enriching patient engagement and advancing clinical decision-making. However, their successful integration requires rigorous development, adaptation, and evaluation strategies tailored to clinical needs. In this Review, we highlight recent advancements, explore emerging opportunities for LLM-driven innovation, and propose a framework for their responsible implementation in healthcare settings. We examine strategies for adapting LLMs to domain-specific healthcare tasks, such as fine-tuning, prompt engineering, and multimodal integration with electronic health records. We also summarize various evaluation metrics tailored to healthcare, addressing clinical accuracy, fairness, robustness, and patient outcomes. Furthermore, we discuss the challenges associated with deploying LLMs in healthcare--including data privacy, bias mitigation, regulatory compliance, and computational sustainability--and underscore the need for interdisciplinary collaboration. Finally, these challenges present promising future research directions for advancing LLM implementation in clinical settings and healthcare.

Problem

• Adapting LLMs for domain-specific healthcare tasks effectively
• Evaluating LLMs in healthcare for accuracy and fairness
• Addressing deployment challenges like privacy and regulatory compliance

Innovation

• Fine-tuning LLMs for healthcare tasks
• Multimodal integration with health records
• Evaluating clinical accuracy and fairness