Diabetes is a major cause of vision loss globally, yet this devastating complication is largely preventable. Early detection and treatment of diabetic retinopathy necessitates screening. Ocular imaging is widely used clinically, both for the screening and management of diabetic retinopathy. Common eye conditions, such as glaucoma, cataracts, and retinal vessel thrombosis, and signs of systemic conditions, such as hypertension, are frequently revealed. As well as imaging by a skilled clinician during an eye examination, non-ophthalmic clinicians, such as general practitioners, endocrinologists, nurses, and trained health workers, can also perform diabetic eye screening. This process usually comprises local imaging with remote grading, mostly human-grading. However, grading incorporating artificial intelligence is emerging. In a clinical research context, retinal vasculature analyses using semi-automated software in many populations have identified associations between retinal vessel geometry, such as vessel caliber, and risk of diabetic retinopathy and other chronic complications of type 1 diabetes and type 2 diabetes. Similarly, evaluation of corneal nerves by corneal confocal microscopy is revealing diabetes-related abnormalities and associations with and predictive power for other chronic diabetes complications. As yet, the value of retinal vessel geometry and corneal confocal microscopy measures at an individual level is uncertain. In this article, targeting non-ocular clinicians and researchers, we review existent and emerging ocular imaging and grading tools, including artificial intelligence, and their associations between ocular imaging findings and diabetes and its chronic complications.