What can GPT-4 do for Diagnosing Rare Eye Diseases? A Pilot Study

Abstract

Abstract Introduction Generative pretrained transformer-4 (GPT-4) has gained widespread attention from society, and its potential has been extensively evaluated in many areas. However, investigation of GPT-4’s use in medicine, especially in the ophthalmology field, is still limited. This study aims to evaluate GPT-4’s capability to identify rare ophthalmic diseases in three simulated scenarios for different end-users, including patients, family physicians, and junior ophthalmologists. Methods We selected ten treatable rare ophthalmic disease cases from the publicly available EyeRounds service. We gradually increased the amount of information fed into GPT-4 to simulate the scenarios of patient, family physician, and junior ophthalmologist using GPT-4. GPT-4’s responses were evaluated from two aspects: suitability (appropriate or inappropriate) and accuracy (right or wrong) by senior ophthalmologists (> 10 years’ experiences). Results Among the 30 responses, 83.3% were considered "appropriate" by senior ophthalmologists. In the scenarios of simulated patient, family physician, and junior ophthalmologist, seven (70%), ten (100%), and eight (80%) responses were graded as “appropriate” by senior ophthalmologists. However, compared to the ground truth, GPT-4 could only output several possible diseases generally without “right” responses in the simulated patient scenarios. In contrast, in the simulated family physician scenario, 50% of GPT-4's responses were “right,” and in the simulated junior ophthalmologist scenario, the model achieved a higher “right” rate of 90%. Conclusion To our knowledge, this is the first proof-of-concept study that evaluates GPT-4’s capacity to identify rare eye diseases in simulated scenarios involving patients, family physicians, and junior ophthalmologists. The results indicate that GPT-4 has the potential to serve as a consultation assisting tool for patients and family physicians to receive referral suggestions and an assisting tool for junior ophthalmologists to diagnose rare eye diseases. However, it is important to approach GPT-4 with caution and acknowledge the need for verification and careful referrals in clinical settings.

Keywords

• Rare eye disease
• Generative pretrained transformer
• Large language models
• Artificial intelligence