AI-assisted diagnosis from eye scans
The problem
Demand for expert reading of eye scans outstrips specialist capacity, delaying referral for sight-threatening conditions.
The AI approach
A deep-learning system analyses 3D optical-coherence-tomography (OCT) scans, using an interpretable two-stage design (segmentation, then referral recommendation) that can also express uncertainty.
Evidence it works
In a 2018 Nature Medicine study, the system recommended the correct referral decision for 50+ sight-threatening conditions, matching world-leading expert ophthalmologists (reported ~94% accuracy on the referral decision).
What “good” looks like
Expert-level triage that prioritises urgent cases, with transparent intermediate outputs clinicians can inspect and an explicit “refer to a human when unsure.”
Feasibility & cost shape
High regulatory and validation cost; value is greatest where specialist capacity is the bottleneck and scan volumes are high.
A benchmark for narrow, high-accuracy, interpretable clinical decision support — and for being clear-eyed about the path from a strong study to a deployed system.
Based on publicly reported information about the DeepMind with Moorfields Eye Hospital work.
This is an industry example included for illustration. It is not a Leia Intelligence project, and no client of ours is implied. Figures are as publicly reported by the original parties.
Sources: DeepMind / Moorfields · De Fauw et al., *Nature Medicine* (2018)