Role prompting modulates linguistic style but not clinical decision structure in GPT-5 tumour board simulation
TL;DR
GPT-5 reliably adapts linguistic style to clinical personas but produces limited specialty-specific output diversity, supporting its role as a decision-support adjunct rather than an autonomous specialist simulator.
Abstract
Multidisciplinary tumour boards (MDTs) are the standard for gastrointestinal oncological decision-making but remain resource-intensive. Whether specialty-specific role prompting induces genuinely distinct clinical reasoning in large language models (LLMs)—or merely role-appropriate language around an invariant output—has not been systematically tested. We applied five zero-shot prompting frameworks and a majority-vote ensemble to GPT-5 across 100 gastrointestinal oncology cases with MDT-validated decisions: a simulated MDT, multi-expert deliberation, three specialist personas, and a majority-vote ensemble. Concordance with MDT recommendations ranged from 78% to 87%, with no significant inter-framework differences (Cochran’s Q = 8.46, p = 0.133). Specialty-characteristic language was near-universal (97–100%) but uncorrelated with accuracy. Embedding analysis revealed high semantic similarity across personas (cosine similarity 0.805–0.836; η² = 0.049), contrasting with substantially greater output separation under multi-expert deliberation (η² = 0.554–0.581). GPT-5 reliably adapts linguistic style to clinical personas but produces limited specialty-specific output diversity, supporting its role as a decision-support adjunct rather than an autonomous specialist simulator.