The AI Ophthalmology & Vision Research Intelligence agent — retina to real-world.
A scientific research agent (not a chatbot, not a diagnostic tool) that discovers, connects, analyses, challenges and synthesises ophthalmology & vision-science knowledge from authorized literature, databases, datasets and enterprise tools — to help you understand what is known, what is unknown, what may be true, what has failed and what to test next.
Twelve deeply-connected knowledge domains spanning the research lifecycle.
Age-related macular degeneration, diabetic retinopathy & DME, glaucoma, inherited retinal diseases, dry eye, uveitis and corneal & lens disorders across the vision lifespan.
Retinal & photoreceptor biology, RPE, choroidal vasculature, aqueous dynamics & IOP, the ocular immune-privilege environment, neuro-retinal degeneration and angiogenesis.
VEGF & complement biology, geographic atrophy, retinal neurodegeneration and fibrosis — mechanism → biomarker → target → therapy → outcome across leading causes of blindness.
Genomics (AMD & IRD variants, PRS), single-cell & spatial retinal atlases, aqueous/vitreous proteomics and metabolomics — connected gene → cell → retina → vision.
Human-genetic, functional and multi-omics evidence for ocular targets — with tractability, ocular exposure, safety and prior failures clearly separated.
Anti-VEGF & complement inhibitors, small molecules, biologics, AAV gene therapy and cell therapy — a leading field for ocular gene therapy — across the development cascade.
Intravitreal injection, sustained-release implants & port-delivery, suprachoroidal, topical and gene-delivery routes — the delivery science that shapes durability and burden.
OCT & OCT-angiography, fundus autofluorescence, visual-function and structural endpoints, and genetic biomarkers — with performance, validation and clinical-utility context.
Design, population, endpoints (BCVA letters, GA lesion growth, IOP), comparators, results and adverse events — surfacing failed hypotheses and design opportunities.
Vision-outcome & lesion-progression modelling, survival analysis, imaging analytics, causal inference and multi-omics integration on authorized ophthalmology datasets.
Reasons across Disease ↔ Gene ↔ Cell ↔ Pathway ↔ Biomarker ↔ Target ↔ Drug ↔ Delivery ↔ Trial ↔ Outcome, with every claim traceable to primary sources.
Finds conflicting findings, dry-AMD mechanism gaps, missing functional biomarkers and poorly-validated targets — then frames testable hypotheses and experiments.
Surface novel, testable research directions and drug-repurposing leads across the whole ecosystem — not a single paper.
Integrate literature, omics, trials and real-world data into one traceable evidence graph so hidden connections become visible.
A built-in scientific critic tests for confounding, batch effects, power, publication bias, replication and prior failures.
Research questions, proposals, protocols, SAPs, reviews and manuscript scaffolds — never fabricating data, patients, stats or citations.
Statements are tagged so you always know their standing — and confidence is stated, never inflated. Citations, DOIs, PMIDs, dataset and trial IDs are drawn from primary sources; if a source can't be verified it says so. Ophthalmology Genius is a research-intelligence system, not a substitute for a physician — it does not diagnose or treat individuals.
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