ArticleIndian journal of dermatology
A Comparative Study: Can Large Language Models be a Supportive Tool in the Diagnosis and Treatment of Scabies?
Article in Indian journal of dermatology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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: Claude 3 Opus achieved the highest accuracy (89%), significantly outperforming other LLMs and dermatologists (75%) ( 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.
Indexed as
Identifiers
What OpenQuestion holds
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.