AI in conjunctivitis research: assessing ChatGPT and DeepSeek for etiology, intervention, and citation integrity via hallucination rate analysis
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.
Authors
- Muhammad Hamza Mahmood (ORCID: https://orcid.org/0009-0007-8839-6618)
- Khursheed Aurangzeb (ORCID: https://orcid.org/0000-0003-3647-8578)
- Musaed A. Alhussein (ORCID: https://orcid.org/0000-0002-5538-6778)
- Muhammad Hasnain (ORCID: https://orcid.org/0009-0003-5702-9621)
- Imran Ghani (ORCID: https://orcid.org/0009-0000-9571-7036)
Institutions
- Virginia Military Institute (US)
- Lahore Leads University (PK)
- King Saud University (SA)
Publication Details
- Journal
- Frontiers in Artificial Intelligence
- Published
- 2025-08-20
- DOI
- https://doi.org/10.3389/frai.2025.1579375
- Citations
- 2
- Primary Topic
- Clinical Reasoning and Diagnostic Skills
- Type
- article
- Field-Weighted Citation Impact
- 3.03
Funders
- King Saud University
- King Faisal University