Role-Prompting in Frontier Large Language Models Influences Clinical Reasoning in Complex Medical Cases

Abstract

Background: Large language models (LLMs) are increasingly deployed in healthcare, where they may adopt different stakeholder perspectives, yet the effect of role-prompting on clinical ethical reasoning remains poorly characterized. Methods: We evaluated three frontier LLMs: Claude Opus 4.6, GPT-5.4, and Gemini 3.1 Pro across 25 ethically complex medical cases. Each model responded from three stakeholder perspectives (physician, patient, insurer) across three independent runs (675 total responses). Decisions were benchmarked against a six-physician panel. Ethical value prioritization was analyzed from physician- and LLM-provided ranked values. A Patient-Centric Decision Index (PCDI) was developed to quantify LLM decision alignment with patient-preferred out-comes. Results: Among 20 cases with clear physician consensus, LLMs prompted as an insurer reduced alignment with physician majority by 50% for GPT-5.4 (p = 0.004), 45% for Gemini 3.1 Pro (p = 0.008) and 10.5% (NS) for Opus 4.6. The insurer role shifted primary ethical values from beneficence (27%) to financial stewardship (20%) across all LLMs. Conclusions: Stakeholder role-prompting fundamentally alters clinical decisions and ethical value frameworks of frontier LLMs, with the insurer role producing systematic denial of physician-endorsed, patient-preferred treatments. These findings raise the need for standardized LLM patient-centricity benchmarks, and physician oversight when LLMs are deployed in clinical decision-making.