A Comparative Study: Can Large Language Models be a Supportive Tool in the Diagnosis and Treatment of Scabies?
Year: 2026
Type: article
Abstract: Background: The advent of large language models (LLMs) has endowed artificial intelligence with remarkable capabilities to comprehend and generate human-like text by leveraging extensive datasets. Numerous studies in medicine increasingly focus on LLMs, particularly highlighting their substantial potential in diagnostic and therapeutic management. Scabies is a contagious skin disease that continues to affect over 200 million people globally each year and is particularly prevalent in developing countries. In recent years, there has been a marked increase in scabies cases both in Turkey and worldwide, elevating scabies to the status of a public health concern. Therefore, the Clinical Practice Guideline for the Diagnosis and Treatment of Scabies (CPGDT) was published in 2024. Aims and Objectives: This study evaluates the performance of LLMs on scabies-related questions created from CPGDT and compares it to a dermatologist. Materials and Methods: Sixty-four multiple-choice questions in English were administered to nine LLMs, including Claude 3 Opus, ChatGPT (versions 3.5, 4, and 4o), Google Gemini (1.0 and 1.5 Pro), Microsoft Copilot, Meta LLaMA 3 70B, and Perplexity, as well as to a senior dermatologist with 22 years of experience in dermatology. Responses were scored as correct or incorrect. Statistical analyses, including the McNemar test, were conducted using IBM SPSS Statistics 23.0. Results: > 0.05). Conclusion: This is the first study evaluating LLMs' performance on scabies-related knowledge. Claude 3 Opus' superior performance highlights its potential in dermatology, surpassing widely recognized LLMs like ChatGPT. Claude 3 Opus demonstrated exceptional accuracy in scabies-related queries, underscoring its value as adjunctive tool in dermatology. Enhancing LLMs' visual diagnostic capabilities is vital for their integration into clinical practice.
Source: Indian Journal of Dermatology
Authors: İstem Ş. Çamur, Eren Çamur, Duru T. Onan
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7141068346