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McCarthy et al. provide a timely and practical framework for clinicians to appraise artificial intelligence (AI) imaging models, emphasizing training data, performance metrics, and prospective trials before clinical implementation.1 Their glaucoma-focused examples and stepwise tables will be especially useful for ophthalmologists who are new to AI. Building on their guide, I … [Read more...] about Re: McCarthy et al.: A Practical Guide to Evaluating Artificial Intelligence Imaging Models in Scientific Literature

We thank Dr. Bair for the thoughtful and constructive response to our paper and for extending the discussion to the critical issue of post-deployment performance monitoring for ophthalmic artificial intelligence (AI) systems. As highlighted in the letter, there is growing recognition across medicine that clinical AI models may exhibit performance drift over time due to changes … [Read more...] about Re: Bair: From One-Time Validation to Continuous Surveillance for Ophthalmic AI

To evaluate the efficacy of intraocular pressure (IOP)-lowering medications in slowing down axial elongation among adults with progressive high myopia (PHM). … [Read more...] about Intraocular pressure-lowering therapy slows down axial elongation in progressive high myopia with enhanced choroidal perfusion

To evaluate contemporary deep-reasoning LLMs for early assessment of ophthalmic emergencies using sequential, workflow-mimicking information levels. … [Read more...] about Comparative Evaluation of Deep-Reasoning Large Language Models for Ophthalmic Emergencies

To propose and evaluate the risk-stratification potential of Fundus Stretch Index (FunSI), a centile-based retinal metric that quantifies the geometric deviation of the fundus from what is expected for a given spherical equivalent refraction (SER). … [Read more...] about Fundus Stretch Index: a centile-based retinal measure of myopia