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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 suggest that evaluation of ophthalmic AI should explicitly extend beyond deployment, incorporating ongoing “AI quality assurance” analogous to post-market surveillance for drugs.