Promises and challenges of applying large language models in the healthcare domain
Abstract
Large language models are rapidly moving from theoretical concepts to active clinical pilots. Current approaches diverge between general-purpose models, which adapt to healthcare via prompt engineering, and domain-specific models, which prioritize deep alignment with medical knowledge graphs to ensure safety. Despite reported benefits in documentation efficiency and diagnostic reasoning, significant challenges remain regarding hallucination, privacy, and the validity of evaluation metrics. This Mini Review synthesizes current evidence, contrasts these two modeling paradigms, highlights key controversies, and maps out future development routes including retrieval-augmented generation and agentic architectures.
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