Helping specialists see urgency.
Researchers demonstrated deep-learning diagnosis and referral recommendations from retinal scans.
Original sources ↓ · Revision history ↓
Demonstrated · source published 2018-08-13
The human problem
Specialists must identify which eye conditions need urgent attention.
The prior constraint
Clinicians interpret detailed optical coherence tomography scans.
AI’s actual role
A two-stage model mapped scan features to diagnoses and referral suggestions.
The documented result
The system demonstrated strong retrospective referral performance.
Why it may matter
It suggested a possible tool for supporting specialist triage.
Limitations
Retrospective performance does not establish routine clinical safety or benefit.
Unresolved questions
Evaluate clinical workflows and outcomes prospectively.
Source history & evidence assessment
- Maturity
- Demonstrated
- Claim confidence
- unassessed
- Event date
- Not recorded
- Source published
- 2018-08-13
- Captured
- Not recorded
- Last source review
- 2026-09-05
- Editorial method
- Original source check
- Place / relevance
- London, United Kingdom · institution-location
Legacy source check; no named human reviewer is recorded in this projection.
Maturity describes the tested or operational setting. Confidence describes support for the particular claim; one does not determine the other.
Original sources
Clinically applicable deep learning for diagnosis and referral in retinal disease ↗ · paper
Institutions: Moorfields Eye Hospital & DeepMind
Explore the underlying question
Related developments
Editorial connections between distinct settings and results; these links do not imply replication.
Revision & correction history
No corrections recorded.