AI in conjunctivitis research: assessing ChatGPT and DeepSeek for etiology, intervention, and citation integrity via hallucination rate analysis

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.