Aligning Large Language Models with Healthcare Stakeholders: A Pathway to Trustworthy AI Integration

Healthcare professionals must be involved throughout the entire AI development process, not just at the end. The paper emphasizes that doctors and other stakeholders need to participate in training data selection, model training, and how the AI makes decisions—ensuring AI systems reflect actual medical knowledge and clinical needs from the start.

LLMs don't automatically understand healthcare values and priorities. The paper identifies a key problem: language models may produce outputs that don't match what healthcare professionals actually need or believe is safe, highlighting that alignment between AI behavior and human expertise requires intentional design rather than occurring naturally.

Three specific areas drive better alignment: healthcare knowledge, task understanding, and human guidance. The research demonstrates that LLMs perform more responsibly when they're enhanced with medical information, clearly understand what clinical tasks they're being asked to do, and receive ongoing direction from human professionals.

Human oversight is essential for trustworthy healthcare AI. Rather than viewing AI as autonomous, the paper positions healthcare professionals as active guides throughout an AI system's lifecycle, suggesting that responsible AI integration depends on maintaining meaningful human control and participation.

Alignment is foundational to safe AI adoption in medicine. The paper argues that ensuring AI systems match stakeholder values and knowledge isn't an optional feature but a prerequisite for deploying AI responsibly in real healthcare settings where decisions affect patient safety.