Large language models in clinical applications

Abstract

Large language models (LLMs) are emerging as the cognitive engine of next-generation clinical decision-support systems (CDSS), delivering real-time insights that shape therapy choices, improve outcomes, and raise the overall quality of care. This chapter offers a systematic, practice-oriented assessment of what state-of-the-art LLMs can, and still cannot, do across the clinical continuum. We show how foundation models sharpen diagnostics, draft and structure electronic health record (EHR) notes, power patient-facing chatbots, personalize medical education, and accelerate evidence retrieval and knowledge management, thereby improving both clinical throughput and documentation accuracy. We investigate the processes that underpin these advances, including retrieval-augmented generation (RAG), chain-of-thought (CoT) prompting, and multi-agent orchestration, which together help reduce hallucinations and surface transparent citations, but remain vulnerable to factual slips, demographic bias, and alert fatigue. A dedicated section reviews the leading public benchmarks and the rise of “LLM-as-a-judge” protocols, which now play a significant role in rating reasoning consistency, safety, fairness, and explainability. By mapping achievements, caveats, and open research fronts in one place, this chapter aims to equip health-system leaders, developers, and educators with a realistic and actionable blueprint for deploying LLMs as dependable, equitable, and energy-aware partners across a range of healthcare settings, from resource-limited clinics to research-intensive academic hospitals. Through this lens, we hope to capture both the immense promise and the practical challenges that define the journey toward truly intelligent clinical decision support.

Ähnliche Arbeiten