Large language models in Hidradenitis suppurativa: strong empathy, weak exposome awareness

As patients increasingly consult large language models (LLMs) for health information, their capacity to provide clinically appropriate, empathic, readable and exposome-aware counseling requires evaluation.</p> <p>To benchmark LLM-generated responses to simulated HS consultations for clinical performance, exposome awareness, quality-of-life (QoL) coverage and readability.</p> <p>In this blinded cross-sectional study, five LLMs (Claude-4.5 Sonnet, Gemini-3.0 Pro, ChatGPT-5.2, DeepSeek-V3.2 and Llama-4.0 Maverick 17B) generated zero-shot, single-turn responses to 47 clinically and socially complex HS cases.

Three independent dermatologists rated responses across six domains: clinical accuracy, understandability, shared decision-making, actionability, exposome awareness and clinical empathy. The primary outcome was the mean six-domain Likert score, rescaled to a 20–100 normalized score. Secondary outcomes included domain scores, QoL coverage, readability and response length.</p> <p>Overall, 235 responses yielded 705 dermatologist evaluations (ICC[3,k] = 0.814; 95% CI, 0.797–0.831).