Conversational AI Models for ophthalmic diagnosis: Comparison of ChatGPT and the Isabel Pro Differential Diagnosis Generator

Abstract

With the rapidly growing field of conversational artificial intelligence (AI), it is becoming increasingly likely that these technologies will revolutionize the way physicians diagnose and treat patients. The purpose of this study is to evaluate the use of conversational AI language models, specifically ChatGPT, for the diagnosis of ophthalmic disease, and to compare it to existing tools, namely the Isabel Pro Differential Diagnosis Generator. Prospective, comparative evaluation of ChatGPT and Isabel in formulating provisional and differential diagnoses from a set of rich text case report descriptions. Ten ophthalmology patient cases were selected at random from a publicly available online database of ophthalmic case reports. The text details of each case were input into ChatGPT and Isabel. Their ability to identify the actual diagnosis and provide relevant differential diagnoses was compared. ChatGPT identified the correct diagnosis in 9/10 cases while having the correct diagnosis listed in all 10/10 of its lists of differentials. Isabel identified only 1/10 provisional diagnoses correctly, however it included the correct diagnosis in 7/10 of its differential diagnosis lists. The median position of the correct diagnosis in the ranked differential lists was 1.0 (IQR 1.0 to 2.8) for ChatGPT versus 5.5 (IQR 3.3 to 10.0) for Isabel. Conversational AI models such as ChatGPT have potential value in the diagnosis of ophthalmic conditions, particularly for primary-care providers. As further iterations of models are deployed, additional studies investigating their capabilities are needed to determine the best ways to integrate them into practice.