ArticleHealth care science2026
Preferences of Chinese Dermatologists for Large Language Model Responses in Clinical Psoriasis Scenarios: A Nationwide Cross-Sectional Survey in China.
Article in Health care science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Large language models (LLMs) have shown considerable promise in supporting clinical decision-making. However, their adoption and evaluation in dermatology remains limited. This study aimed to explore the preferences of Chinese dermatologists regarding LLM-generated responses in clinical psoriasis scenarios and to assess how they prioritize key quality dimensions, including accuracy, traceability, and logicality. Methods: A cross-sectional, web-based survey was conducted between December 25, 2024, and January 22, 2025, following the Checklist for Reporting Results of Internet E-Surveys guidelines. A total of 1247 valid responses were collected from practicing dermatologists across 33 of China's provincial-level administrative divisions. Participants evaluated responses to five categories of clinical questions (etiology, clinical presentation, differential diagnosis, treatment, and case study) generated by five LLMs: ChatGPT-4o, Kimi.ai, Doubao, ZuoYiGPT, and Lingyi-agent. Statistical associations between participant characteristics and model preferences were examined using chi-square tests. Results: ChatGPT-4o (Model 1) emerged as the most preferred model across all clinical tasks, consistently receiving the highest number of votes in case study ( Conclusions: Chinese dermatologists suggest a strong preference for ChatGPT-4o over domestic LLMs in psoriasis-related clinical tasks. While accuracy remains the primary criterion, traceability and logicality are also critical, particularly for clinicians in lower-tier hospitals. These findings suggest that future clinical LLMs should prioritize not only content accuracy but also source transparency and structural clarity to meet the diverse needs of different clinical settings.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.