ArticleFrontiers in artificial intelligence2025
AI in conjunctivitis research: assessing ChatGPT and DeepSeek for etiology, intervention, and citation integrity via hallucination rate analysis.
Article in Frontiers in artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
- Performance of deepseek-R1 and ChatGPT-5.4 thinking in the medical laboratory professional title examination: accuracy, stability, and comparison with interns.Frontiers in digital health · 2026Article
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Authors and funding
5 authors.
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Abstract
Introduction: The advent of large language models and their applications have gained significant attention due to their strengths in natural language processing. Methods: In this study, ChatGPT and DeepSeek are utilized as AI models to assist in diagnosis based on the responses generated to clinical questions. Furthermore, ChatGPT, Claude, and DeepSeek are used to analyze images to assess their potential diagnostic capabilities, applying the various sensitivity analyses described. We employ prompt engineering techniques and evaluate their abilities to generate high quality responses. We propose several prompts and use them to answer important information on conjunctivitis. Results: Our findings show that DeepSeek excels in offering precise and comprehensive information on specific topics related to conjunctivitis. DeepSeek provides detailed explanations and in depth medical insights. In contrast, the ChatGPT model provides generalized public information on the infection, which makes it more suitable for broader and less technical discussions. In this study, DeepSeek achieved a better performance with a 7% hallucination rate compared to ChatGPT's 13%. Claude demonstrated perfect 100% accuracy in binary classification, significantly outperforming ChatGPT's 62.5% accuracy. Discussion: DeepSeek showed limited performance in understanding images dataset on conjunctivitis. This comparative analysis serves as an insightful reference for scholars and health professionals applying these models in varying medical contexts.
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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.