Harm Reduction Strategies for Thoughtful Use of Large Language Models in the Medical Domain: Perspectives for Patients and Clinicians.

Abstract

The integration of large language models (LLMs) into health care presents significant risks to patients and clinicians, inadequately addressed by current guidance. This paper adapts harm reduction principles from public health to medical LLMs, proposing a structured framework for mitigating these domain-specific risks while maximizing ethical utility. We outline tailored strategies for patients, emphasizing critical health literacy and output verification, and for clinicians, enforcing "human-in-the-loop" validation and bias-aware workflows. Key innovations include developing thoughtful use protocols that position LLMs as assistive tools requiring mandatory verification, establishing actionable institutional policies with risk-stratified deployment guidelines and patient disclaimers, and critically analyzing underaddressed regulatory, equity, and safety challenges. This research moves beyond theory to offer a practical roadmap, enabling stakeholders to ethically harness LLMs, balance innovation with accountability, and preserve core medical values: patient safety, equity, and trust in high-stakes health care settings.