ABSTRACT
Introduction Assessing the capabilities of ChatGPT-4.0 and ChatGPT-3.5 for diagnosing corneal eye diseases based on case reports and compare with human experts.
Methods We randomly selected 20 cases of corneal diseases including corneal infections, dystrophies, degenerations, and injuries from a publicly accessible online database from the University of Iowa. We then input the text of each case description into ChatGPT-4.0 and ChatGPT3.5 and asked for a provisional diagnosis. We finally evaluated the responses based on the correct diagnoses then compared with the diagnoses of three cornea specialists (Human experts) and evaluated interobserver agreements.
Results The provisional diagnosis accuracy based on ChatGPT-4.0 was 85% (17 correct out of 20 cases) while the accuracy of ChatGPT-3.5 was 60% (12 correct cases out of 20). The accuracy of three cornea specialists were 100% (20 cases), 90% (18 cases), and 90% (18 cases), respectively. The interobserver agreement between ChatGPT-4.0 and ChatGPT-3.5 was 65% (13 cases) while the interobserver agreement between ChatGPT-4.0 and three cornea specialists were 85% (17 cases), 80% (16 cases), and 75% (15 cases), respectively. However, the interobserver agreement between ChatGPT-3.5 and each of three cornea specialists was 60% (12 cases).
Conclusions The accuracy of ChatGPT-4.0 in diagnosing patients with various corneal conditions was markedly improved than ChatGPT-3.5 and promising for potential clinical integration.
Key summary points
- The aim of this work was to evaluate the performance of ChatGPT-4 and ChatGPT-3.5 for providing the provisional diagnosis of different corneal eye diseases based on case descriptions and compared them with three cornea specialists.
- The accuracy of ChatGPT-4.0 in diagnosing patients with various corneal conditions was significantly better than ChatGPT-3.5 based on the specific cases.
- The interobserver agreement between ChatGPT-4.0 and ChatGPT-3.5 was 65% while the interobserver agreement between ChatGPT-4.0 and three cornea specialists were 85%, 80%, and 75%, respectively.
Competing Interest Statement
Mohammad Delsoz: None. Yeganeh Madadi: None Wuqaas M Munir: None Brendan Tamm: None Shiva Mehravaran: None Mohammad Soleimani: None Ali Djalilian: None Siamak Yousefi: Remidio, M&S Technologies, Visrtucal Fields, InsihgtAEye, Enolink
Funding Statement
This work was supported by NIH Grants R01EY033005 (SY), R21EY031725 (SY), grants from Research to Prevent Blindness (RPB), New York (SY), and supports from the Hamilton Eye Institute (SY). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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Abbreviations
- LLM
- Large Language Model
- AI
- Artificial Intelligence
- ChatGPT
- Chat Generative Pretrained Transformer
- CK
- Infectious Crystalline Keratopathy
- PPCD
- Posterior Polymorphous Corneal Dystrophy
- PBK
- Pseudophakic Bullous Keratopathy
- SND
- Salzmann’s Nodular Degeneration
- IRB
- Institutional Review Board
- RLHF
- Reinforcement Learning from Human Feedback
- NLP
- Natural Language Processing
- FECD
- Fuchs’ Endothelial Corneal Dystrophy
- MCD
- Meesmann Corneal Dystrophy
- CHED
- Congenital Hereditary Endothelial Dystrophy
