Study Finds Persona Conditioning Impacts Clinical LLM Performance Differently Across Settings

Global: Study Finds Persona Conditioning Impacts Clinical LLM Performance Differently Across Settings

A team of researchers released a preprint on arXiv in January 2026 that examines how assigning professional personas to large language models (LLMs) influences their behavior in clinical decision‑making tasks. The study evaluates both emergency department physician and nursing personas, as well as distinct interaction styles described as “bold” and “cautious,” across a range of medical triage and patient‑safety scenarios.

Methodology and Evaluation Framework

The authors conducted systematic experiments using multiple LLM architectures, measuring task accuracy, calibration, and risk‑related behavior. Performance was assessed on two primary task categories: high‑acuity triage decisions typical of emergency care and routine assessments common in primary‑care settings. Human clinicians also reviewed a subset of model outputs, providing safety compliance ratings and confidence assessments.

Improved Outcomes in Critical‑Care Scenarios

According to the preprint, medical personas yielded up to a 20 % increase in both accuracy and calibration when applied to emergency‑department triage tasks. The researchers attribute these gains to the alignment of model behavior with domain‑specific expectations embedded in the persona prompts.

Degraded Performance in Primary‑Care Contexts

Conversely, the same medical personas were associated with comparable declines—approximately 20 %—in accuracy and calibration on primary‑care tasks. The authors note that the effect appears context‑dependent, suggesting that persona conditioning does not uniformly enhance model competence.

Interaction Style Modulates Risk Propensity

Experiments comparing “bold” versus “cautious” interaction styles revealed model‑specific variations in risk‑taking behavior. Some models exhibited heightened sensitivity to safety‑related cues under a cautious style, while others showed minimal change, indicating that interaction style interacts complexly with model architecture.

Human Clinician Agreement and Confidence

Human reviewers demonstrated moderate agreement on safety compliance, with an average Cohen’s kappa of 0.43. Clinicians reported low confidence in 95.9 % of their responses regarding the reasoning quality of the LLM outputs, highlighting potential gaps between model performance metrics and human interpretability.

Implications and Availability

The authors conclude that persona conditioning functions as a behavioral prior that introduces trade‑offs rather than guaranteeing expertise or safety. The study’s code repository is publicly accessible at https://github.com/rsinghlab/Persona_Paradox, enabling further replication and analysis.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.