CorneaIx uses ophthalmic AI outputs and past investigation history to suggest the next best investigation, improve disease detection, and support more confident clinical decisions.
In many eye-care workflows, clinicians must make decisions using incomplete context. Important historical investigations may be scattered across systems, while image-based findings are reviewed separately from prior reports and test results.
Prior AS-OCT, topography, confocal, and culture reports live in different files, folders, and systems — often unreachable during a live consultation.
An AI image detection is most useful when paired with prior reports — but most platforms return the model output without referencing the patient's history.
Choosing the next investigation — repeat AS-OCT, confocal, culture, or referral — depends on recall and pattern recognition that's hard to standardise.
Care episodes are disconnected — first visit, follow-up, and procedure records rarely surface together when a clinician needs to make the next decision.
CorneaIx sits on top of our core ophthalmic AI vision system. It reads model output from slit-lamp-based disease detection, analyzes past investigations, and suggests the most relevant next diagnostic step.
This makes the platform more than a detection engine. It becomes a clinical reasoning layer that helps eye-care teams move from image interpretation to better investigation planning and disease confirmation.
CorneaIx improves disease detection by adding context. A single image can be useful, but the combination of image output, prior tests, and investigation history gives clinicians a much stronger basis for decision-making.
Joining today's AI output with prior patterns helps surface changes that would otherwise be missed across separate visits.
Recommendations backed by prior history give clinicians stronger grounds to commit to a decision — or escalate.
Avoids redundant or poorly-timed tests by recommending the next investigation that adds the most clinical signal.
Especially valuable in busy OPDs where fast, informed decisions matter and continuity across visits is hard to maintain manually.
After AI flags a finding, CorneaIx joins it with prior investigations and surfaces the next test most likely to add clinical signal.
When escalating to a specialist, CorneaIx assembles the relevant history so the referral arrives with the right context attached.
Today's AI finding plus last quarter's pattern often tells a different story than the image alone — and CorneaIx brings that to the surface.
When throughput matters, CorneaIx helps clinicians triage intelligently, reducing revisits and unclear next steps.
Same context for technicians triaging images and clinicians making decisions — fewer handoff gaps, fewer dropped findings.
CorneaIx is especially useful where clinical data is spread across multiple visits, devices, or providers. By using past investigations and AI output together, it helps care teams avoid missing patterns that matter for diagnosis.
Pattern recognition across visits helps clinicians commit to the right next step with more evidence behind it.
Less time spent re-deriving history means more time spent on the patient and the decision in front of you.
Recommendations are grounded in the same evidence every time — reducing variability between clinicians and clinics.
CorneaIx is the intelligence layer that makes our ophthalmic AI more clinically useful. It turns vision model output into actionable next steps and helps clinicians detect disease with greater confidence.