ArticleNature medicine2018
Clinically applicable deep learning for diagnosis and referral in retinal disease.
Article in Nature medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 907 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Multimodal Imaging in Vitreo-retinal Surgery and Macular Dystrophies: Biomarkers of Morpho-Functional Recovery by Artificial Intelligence
Development and Validation of an Artificial Intelligence-assisted Diagnostic System for Ophthalmic Pathologies
Who cites it
907 citing papers in PubMed, 4 syntheses or guidelines pooled it, 2,512 citations in OpenAlex.
- Effectiveness of screening modalities for early detection of diabetic retinopathy: a systematic review and meta-analysis of tele-ophthalmology, AI-based tools, and conventional methods.Frontiers in medicine · 2026Pooled it
- Current landscape and research gaps in artificial intelligence for ophthalmic nursing: a scoping review.Frontiers in medicine · 2026Pooled it
- Performance of artificial intelligence-assisted ultrasound elastography in classifying benign and malignant breast tumors: a systematic review and meta-analysis.BMC medical imaging · 2025Pooled it
- Artificial intelligence for glaucoma.The Cochrane database of systematic reviews · 2025Pooled it
- Tele-ophthalmology-enabled and artificial intelligence-ready referral pathway for community optometry referrals of retinal disease: HERMES cluster randomised trial with a diagnostic accuracy study.Health technology assessment (Winchester, England) · 2025Trial
- Between the hype and harm: does artificial intelligence in health offer solace or further exclusion for marginalised populations in Sub-Saharan Africa? A scoping review.Global health action · 2026Article
- VERIDAH: solving enumeration anomaly-aware vertebra labeling across imaging sequences.European radiology · 2026Article
- Deep Learning for Diagnosis of Choroideremia andOphthalmology science · 2026Article
- Current Trends in AI and Eye Disease Diagnostics.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2026Review
- Beyond Anti-VEGF: Toward Integrated Precision Care in Diabetic Retinal Disease.Pharmaceutics · 2026Review
- Editorial for the Special Issue "Applications of Biomedical Imaging and Sensing Technologies in Disease Diagnosis".Sensors (Basel, Switzerland) · 2026Article
- Multimodal AI in retinal disease: from images to decisions and real-world endpoints.International ophthalmology · 2026Article
- A five-phase evaluation framework for diagnostic and predictive medical artificial intelligence.NPJ digital medicine · 2026Article
- Reliability of the QUADAS-2 checklist in AI-based medical imaging: A comparison of human reviewers and ChatGPT-4o.Imaging science in dentistry · 2026Article
- CARE: Multimodal Alignment Learning for Ophthalmic Imaging via Joint Optimal-Transport Soft Matching and Graph Neural ODE Refinement.Journal of imaging informatics in medicine · 2026Article
- nnMIL: a generalizable multiple instance learning framework for computational pathology.Nature biomedical engineering · 2026Article
- Self-Supervised OCT Representation Learning with Local Dimensionality Regularization for Automated Retinal Disease Diagnosis.Sensors (Basel, Switzerland) · 2026Article
- Artificial intelligence in diabetic retinopathy: from automated screening to risk-stratified care.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Uncertainty-Aware AI-Assisted Diabetic Retinopathy Grading from Fundus Images with Ordinal Conformal Prediction.Diagnostics (Basel, Switzerland) · 2026Article
- Rapid Access Macular Screening and Evaluation: A Datasheet for a Dataset to Diagnose and Triage Macular Diseases.Ophthalmology science · 2026Article
847 more citing papers are in PubMed but not listed here.
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Authors and funding
34 authors at 3 institutions in 1 country.
Funding
Abstract
The volume and complexity of diagnostic imaging is increasing at a pace faster than the availability of human expertise to interpret it. Artificial intelligence has shown great promise in classifying two-dimensional photographs of some common diseases and typically relies on databases of millions of annotated images. Until now, the challenge of reaching the performance of expert clinicians in a real-world clinical pathway with three-dimensional diagnostic scans has remained unsolved. Here, we apply a novel deep learning architecture to a clinically heterogeneous set of three-dimensional optical coherence tomography scans from patients referred to a major eye hospital. We demonstrate performance in making a referral recommendation that reaches or exceeds that of experts on a range of sight-threatening retinal diseases after training on only 14,884 scans. Moreover, we demonstrate that the tissue segmentations produced by our architecture act as a device-independent representation; referral accuracy is maintained when using tissue segmentations from a different type of device. Our work removes previous barriers to wider clinical use without prohibitive training data requirements across multiple pathologies in a real-world setting.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.