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NEW YORK — OpenAI's based GPT-4 can match and in some cases outperform human ophthalmologists in the diagnosis and treatment of patients with glaucoma and retina disease, according to a research.
The study, published in JAMA Ophthalmology, suggests that advanced tools like Artificial Intelligence-(AI) based large language models (LLM), which are trained on vast amounts of data, text, and images, could play an important role in providing decision-making support to ophthalmologists in the diagnosis and management of cases involving glaucoma and retina disorders, which afflict millions of patients.
"The performance of GPT-4 in our study was quite eye-opening," said lead author Andy Huang, ophthalmology resident at New York Eye and Ear Infirmary of Mount Sinai Hospital, US.
"We recognised the enormous potential of this AI system from the moment we started testing it and were fascinated to observe that GPT-4 could not only assist but in some cases match or exceed, the expertise of seasoned ophthalmic specialists," Huang added.
Researchers matched the knowledge of the GPT-4 (Generative Pre-Training–Model 4) with 12 attending specialists and three senior trainees from the Department of Ophthalmology at the Icahn School of Medicine at Mount Sinai.
A basic set of 20 questions (10 each for glaucoma and retina) from the American Academy of Ophthalmology’s list of commonly asked questions by patients was randomly selected, along with 20 de-identified patient cases culled from Mount Sinai-affiliated eye clinics.
The results showed that AI matched or outperformed human specialists in both accuracy and completeness of its medical advice and assessments.
More specifically, AI demonstrated superior performance in response to glaucoma questions and case-management advice, while reflecting a more balanced outcome in retina questions, where AI matched humans in accuracy but exceeded them in completeness.
"AI was particularly surprising in its proficiency in handling both glaucoma and retina patient cases, matching the accuracy and completeness of diagnoses and treatment suggestions made by human doctors in a clinical note format," said Louis R. Pasquale, Deputy Chair for Ophthalmology Research for the Department of Ophthalmology.
The findings "could serve as a reliable assistant to eye specialists by providing diagnostic support and potentially easing their workload, especially in complex cases or areas of high patient volume", Dr. Huang said.
"For patients, the integration of AI into mainstream ophthalmic practice could result in quicker access to expert advice, coupled with more informed decision-making to guide their treatment."