Using ChatGPT-4 in visual field test assessment
The study highlights the potential of large language models like ChatGPT-4 in assessing visual field tests. ChatGPT-4 could interpret numeric data on tests accurately. However, it was inadequate in interpreting deviation and greyscale maps and suggesting a diagnosis according to the defects.
Authors
- Ali Safa Balcı (ORCID: https://orcid.org/0000-0001-9161-7839)
- Tekin Yaşar (ORCID: https://orcid.org/0000-0003-0842-0686)
- Çiğdem Altan (ORCID: https://orcid.org/0000-0002-0908-6619)
- Gülşah Gümüş (ORCID: https://orcid.org/0000-0003-1954-2400)
- Neşe Alagöz (ORCID: https://orcid.org/0000-0002-0372-8967)
- Ihsan Çakır (ORCID: https://orcid.org/0000-0001-5914-3688)
Institutions
- University of Health Science (KH)
- Sağlık Bilimleri Üniversitesi (TR)
- Istanbul Eye Hospital (TR)
Publication Details
- Journal
- Clinical and Experimental Optometry
- Published
- 2025-02-12
- DOI
- https://doi.org/10.1080/08164622.2025.2463518
- Citations
- 4
- Primary Topic
- Glaucoma and retinal disorders
- Type
- article
- Field-Weighted Citation Impact
- 6.83