The Value Sensitivity Gap: How Clinical Large Language Models Respond to Patient Preference Statements in Shared Decision-Making

A research study evaluated how four major large language models (GPT-5.2, Claude 4.5 Sonnet, Gemini 3 Pro, and DeepSeek-R1) respond to patient preferences in clinical decision-making scenarios. While all models acknowledged patient values, they showed modest actual recommendation shifting with value sensitivity indices ranging from 0.13 to 0.27, revealing gaps in how AI systems incorporate patient preferences into medical recommendations.