LLMs and Human-AI Collaboration in Healthcare and Research

Abstract

The rapid evolution of Large Language Models (LLMs) such as GPT-4, Med-PaLM, and BioGPT is reshaping healthcare by transforming how clinical knowledge is generated and applied. This chapter offers an evidence-based analysis of LLMs through technical, ethical, and theoretical lenses, drawing on STS, TAM, and CDST to explain how human–AI collaboration influences clinical practice and governance. It reviews advancements in LLM architectures, multimodal capabilities, and applications in diagnosis, documentation, and biomedical research. Key risks including bias, hallucination, interpretability gaps, and data insecurity are examined within global regulatory frameworks such as the FDA, EU AI Act, and WHO guidelines. The chapter concludes with a Responsible Innovation Framework for Healthcare LLMs (RIF-H), emphasizing anticipation, reflexivity, inclusion, responsiveness, and sustainability. It argues that the future of healthcare AI lies in augmentation, where intelligent systems enhance human expertise, equity, and empathy.

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