ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. Machine Learning and Artificial Intelligence
Volume 8 - 2025 | doi: 10.3389/frai.2025.1579375
AI in Conjunctivitis Research: Assessing ChatGPT and Deepseek for Etiology, Intervention, and Citation Integrity via Hallucination Rate Analysis
Provisionally accepted
- 1Lahore Leads University, Lahore, Pakistan
- 2King Saud University, Riyadh, Riyadh, Saudi Arabia
- 3Virginia Military Institute, Lexington city, Virginia, United States
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The advent of large language models and their applications have gained significant attention due to their strengths in natural language processing. 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 in order 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. Our findings show that Deepseek excels in offering precise and comprehensive information on specific topics related to conjunctivitis. Deepseek provides detailed explanation and in-depth medical insights. Conversely, the ChatGPT model provides generalized public information on the infection, which makes it more suitable for broader and less technical discussions. Deepseek achieved a better performance with a 7% hallucination rate compared to ChatGPT's 13% in this study. Claude demonstrated perfect 100% accuracy in binary classification, significantly outperforming ChatGPT's 62.5% accuracy. 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.
Keywords: ChatGPT, Comprehensiveness, deepseek, Eye infection, Prompts
Received: 19 Feb 2025; Accepted: 17 Jul 2025.
Copyright: © 2025 Hasnain, Aurangzeb, Alhussein, Ghani and Hamza Mahmood. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Muhammad Hasnain, Lahore Leads University, Lahore, Pakistan
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