Artificial intelligence in retinal care: transforming the doctor-patient partnership

Background: The integration of artificial intelligence into retinal practice represents more than a technological advancement; it constitutes an anthropological shift fundamentally redefining the centuries-old therapeutic partnership between physician and patient. Objective: To examine how artificial intelligence integration into retinal care transforms the therapeutic partnership, revealing what this transformation illuminates about the nature of medical knowledge itself and identifying frameworks for conscious implementation. Methods: We conducted a structured narrative review examining AI-based diagnosis and monitoring of diabetic retinopathy and age-related macular degeneration (2018–2025), synthesizing clinical deployment evidence with qualitative implementation studies, adopting an anthropological interpretive stance to examine technology as a mediator of human relationships. Results: FDA-approved AI systems demonstrate robust diagnostic performance for diabetic retinopathy and age-related macular degeneration. AI integration shifts encounters from examination-based to screen-mediated, clinical reasoning from individual to algorithm-guided, and physicians from diagnosticians to interpreters. Three insights emerge. First, AI reveals that medical practice always combined mechanistic reasoning with pattern recognition, now separated algorithmically. Second, accountability operates asymmetrically: while all stakeholders derive benefits from algorithmic integration, authority over system selection and deployment remains concentrated among vendors and institutions rather than distributed to frontline clinicians or patients. Third, impact diverges along existing stratification: transformation may create access for excluded populations while potentially eroding relationships for those who had comprehensive care, raising fundamental questions about equitable distribution. Conclusions: The AI transformation of retinal care offers a revealing mirror of medicine’s algorithmic future. Success demands epistemological rigor, robust evaluation competencies and establishing frameworks for shared accountability among the various stakeholders. Our framework maps six fundamental dimensions where synthesis supersedes substitution: expanding algorithmic capabilities while preserving healing relationships, creating access while maintaining continuity. Medicine can embrace algorithmic intelligence while preserving its humanistic core through conscious choices about epistemology, equity, and the character of practice we create.