Build a Cloud AI OCT-Scan Analysis Platform

1. 1

   Target practicing clinicians, not primary care screening

   This platform is distinct from autonomous screening: it serves optometrists and ophthalmologists who already take OCT scans and want AI help interpreting them, rather than screening primary-care patients without a specialist. That changes the buyer, the regulatory posture (often clinical decision support with a clinician in the loop rather than fully autonomous diagnosis), and the pitch. One documented platform detects more than 70 retinal pathologies and biomarkers. Decide clearly that your user is the eye-care provider.

2. 2

   Build the multi-pathology analysis engine

   The value is breadth and reliability: detecting a wide range of retinal pathologies and biomarkers from OCT scans, and expanding into related analyses such as glaucoma optic-disc assessment. This requires large, well-labeled OCT datasets and strong segmentation and classification models. Depth across many findings is what makes the tool trustworthy at the point of scan. Invest in the data and the range of detectable conditions.

3. 3

   Clear the right regulatory pathway

   A clinician-facing decision-support tool that keeps a licensed provider in the loop can often follow a lighter regulatory path than fully autonomous diagnostic AI, but it is still regulated software and the claims you make define the requirements. Overclaiming diagnosis pulls you into a heavier pathway. Get regulatory advice on exactly what you can market and how. The right positioning is both a compliance and a speed-to-market decision.

4. 4

   Sell the over-referral and confidence value

   The core commercial message is that better point-of-scan analysis lets frontline providers refer to specialists only when truly needed, reducing costly and unnecessary over-referrals while catching real disease. That saves money and builds provider confidence, which is a concrete return you can articulate. This is a SaaS sale to a clinician or practice, priced as a subscription per user, site, or scan. Lead with the workflow and referral economics.

5. 5

   Run the cloud SaaS and expand

   As a cloud subscription serving existing clinicians, this is the more accessible AI model in the eye-care stack: lower capital, faster to revenue, and standard SaaS economics, though still requiring real clinical validation and data security under HIPAA. Growth is more clinicians, more pathologies, more scan types, and integration with the OCT devices and practice systems clinicians already use. Recurring subscription revenue and expanding analysis breadth compound together. Build it as a durable clinical-software company.

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Best for: Medical AI founders building clinician-facing decision-support SaaS

Not for: This is not for someone who wants to build autonomous diagnosis, which carries a far heavier regulatory burden. It is decision support for existing clinicians. If you want autonomous screening for primary care, the diabetic-retinopathy or handheld cards fit instead.