Age-Related Macular Degeneration is a chronic degenerative disease limiting, and eventually incapacitating, the central vision field. Its progression is slow and asymptomatic in the first stages, then the accumulated damages to the macula start to impede the vision. The progression of this disease is not completely linear, due to the two alternative, and potentially coexisting, later forms, named “dry” and “wet”, which present different mechanisms and treatment opportunities.

The diagnosis of Age-Related Macular Degeneration requires an ophthalmic visit by a specialist, who must analyse a huge amount of data with considerable effort. Different models have been proposed to automatically screen/diagnose this pathology using mainly image classification techniques. Unfortunately, the treatments available for the later stages present serious limitations, therefore earlier diagnosis might help to plan a preventive strategy up to slow the progression.

Lastly, a new interest in explainable AI is demanding more transparency in the decision-making process adopted by deep learning models to decide, opening the path for new paradigms to integrate in a smoother way these technological advances in the current medical context.