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Related Concept Videos

Glaucoma: Overview01:25

Glaucoma: Overview

Glaucoma is an eye condition characterized by increased intraocular pressure that damages the retina and optic nerve, leading to irreversible blindness if left untreated. The human eye has various components, including the cornea, iris, pupil, lens, and optic nerve. Aqueous humor is secreted by the epithelium of the ciliary body in the posterior chamber and flows through the trabecular meshwork and canal of Schlemm, maintaining normal intraocular pressure. The trabecular meshwork and the canal...
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Angle-closure glaucoma, or closed-angle glaucoma, is an eye condition where the iris bulges out and blocks the iridocorneal angle, resulting in a buildup of aqueous humor and increased intraocular pressure. Immediate medical attention is necessary due to the sudden onset of symptoms. The treatment for angle-closure glaucoma includes short-term and long-term approaches. Short-term treatment involves using eye drops like pilocarpine to lower intraocular pressure by increasing aqueous humor...

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Multimodal Performance of GPT-4 in Complex Ophthalmology Cases.

David Mikhail1, Daniel Milad2,3, Fares Antaki2,4,5

  • 1Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5S 1A1, Canada.

Journal of Personalized Medicine
|April 25, 2025
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Summary

GPT-4's diagnostic accuracy in ophthalmology decreases with images alone, but improves with descriptions. It shows potential as an assistive tool, comparable to human experts in some reasoning tasks.

Keywords:
LLMartificial intelligencemultimodalophthalmology

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Area of Science:

  • Artificial Intelligence in Medicine
  • Ophthalmology
  • Multimodal AI

Background:

  • Multimodal capabilities in GPT-4 offer advancements in artificial intelligence for ophthalmology.
  • The utility of GPT-4 for complex diagnostic and reasoning tasks in ophthalmology is not fully understood.
  • Evaluating AI performance against human expertise is crucial for clinical integration.

Purpose of the Study:

  • To assess GPT-4's multimodal performance on diagnostic and next-step reasoning in complex ophthalmology cases.
  • To compare GPT-4's accuracy with board-certified ophthalmologists across different input modalities.
  • To identify limitations and potential applications of multimodal AI in ophthalmic diagnostics.

Main Methods:

  • GPT-4 was evaluated on three arms: text with figure descriptions, text with figures, and figures only.
  • Performance was measured by diagnostic and next-step accuracy in complex ophthalmology cases.
  • GPT-4's results were benchmarked against three board-certified ophthalmologists.

Main Results:

  • GPT-4 achieved 38.4% diagnostic accuracy and 57.8% next-step accuracy with figures only.
  • Diagnostic accuracy decreased significantly with figures alone compared to text-only prompts (p=0.007).
  • Adding figure descriptions improved diagnostic accuracy to 49.3%, comparable to text-only prompts.

Conclusions:

  • GPT-4's diagnostic performance declines when relying solely on ophthalmic images, indicating current multimodal limitations.
  • GPT-4 demonstrated comparable diagnostic and next-step reasoning performance to at least one ophthalmologist.
  • GPT-4 shows promise as an assistive tool in ophthalmology, with future research focusing on prompt optimization.