Ethical and Explainable Use of Large Language Models in Healthcare

Abstract

Advent of large language models such as GPT-4, Claude, and Med-PaLM changes healthcare landscape rapidly. These models enable clinical reporting, diagnostics support, knowledge retrieval, and patient education. Impressive qualities of these models are linguistic fluency, probabilistic reasoning, and scalability making them both extremely important but and potentially dangerous. Decision-making in medicine bears irreversible consequences, in life, dignity, and justice, but explainability cannot be left. It becomes an ethical and legal imperative. This chapter discusses accounting for algorithmic bias, risks to data privacy, and misinformation in these approaches. SHAP and LIME help close gap between AI predictions and clinical reasoning processes. Regulations require governance's active participatory and rooted in beneficence, autonomy, and justice. Interdisciplinary diverse validation, open auditing, ethical committees, and patient education is way forward for chapter, setting AI in healthcare up to be powerful, fair, transparent, and trustworthy.

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