ArticleIndian dermatology online journal
A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology.
Article in Indian dermatology online journal. 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
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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.
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Who cites it
1 citing paper in PubMed.
- Artificial intelligence in dermatology: A literature review of current evidence and clinical implementation.JID innovations : skin science from molecules to population health · 2026Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: With the growing interest in generative artificial intelligence (AI), the scientific community is witnessing the vast utility of large language models (LLMs) with chat interfaces such as ChatGPT and Microsoft Bing Chat in the medical field and research. This study aimed to investigate the accuracy of ChatGPT and Microsoft Bing Chat to answer questions on Dermatology, Venereology, and Leprosy, the frequency of artificial hallucinations, and to compare their performance with human respondents. Aim and Objectives: The primary objective of the study was to compare the knowledge and interpretation abilities of LLMs (ChatGPT v3.5 and Microsoft Bing Chat) with human respondents (12 final-year postgraduates) and the secondary objective was to assess the incidence of artificial hallucinations with 60 questions prepared by the authors, including multiple choice questions (MCQs), fill-in-the-blanks and scenario-based questions. Materials and Methods: The authors accessed two commercially available large language models (LLMs) with chat interfaces namely ChatGPT version 3.5 (OpenAI; San Francisco, CA) and Microsoft Bing Chat from August 10 Results: In our testing set of 60 questions, Bing Chat outperformed ChatGPT and human respondents with a mean correct response score of 46.9 ± 0.7. The mean correct responses by ChatGPT and human respondents were 35.9 ± 0.5 and 25.8 ± 11.0, respectively. The overall accuracy of human respondents, ChatGPT and Bing Chat was observed to be 43%, 59.8%, and 78.2%, respectively. Of the MCQs, fill-in-the-blanks, and scenario-based questions, Bing Chat had the highest accuracy in all types of questions with statistical significance ( Limitations: Variability in respondent accuracy, a small question set, and exclusion of newer AI models and image-based assessments. Conclusion: This study showed an overall better performance of LLMs compared to human respondents. However, the LLMs were less accurate than respondents in topics like inflammatory disorders and leprosy. Proper regulations concerning the use of LLMs are the need of the hour to avoid potential misuse.
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Registered trials
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