Large Language Models in Healthcare
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.