OphthalX — retinal imaging
Diabetic retinopathy screening across several retinal imaging modalities at camp scale.
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Prior auth, claims, scheduling and imaging, handled by AI. A clinician says GO or NO-GO on every decision.
The challenge
Coding gaps and payer criteria checks delay authorizations and push up days in A/R.
Screening programmes need radiologists and ophthalmologists who are not available at camp scale.
Scheduling, reminders and follow-up run in separate tools, so adherence drops.
Focus use cases
Identify fraudulent claims and optimize claim processing.
Constraint-solving models that optimize schedules across locations.
Risk models for cancer, heart ailments and fertility outcomes.
Medication adherence reminders and personalized wellness rewards.
Teammate swarm
From the IntelliMesh use-case lab
Diabetic retinopathy screening across several retinal imaging modalities at camp scale.
Chest X-ray and CT analysis for classification, segmentation and prediction.
Mammography triage into benign, malignant and normal cases.
Telemedicine with a prognosis engine, e-prescriptions, scheduling and a chatbot.
Built on acceleratorsComputer Vision & Document AI · Multimodal AI · Enterprise RAG & Knowledge · On-Device Small Language Models
Use cases and accelerators co-developed with our US partner Mintmore Inc. See mintmore.us.
Providers, payers and screening programmes
Vision models screen retinal, chest and breast images at camp scale. A prior-auth swarm cuts denials. A clinician approves every decision.
Start the 10·20·30 Pathway
Other industries
Questions
flock3 teammates process claims, detect fraud, prepare prior authorizations and reduce denials, so staff spend less time on paperwork and more on care.
Yes. Models screen retinal images for diabetic retinopathy, classify chest X-ray and CT scans, and triage mammograms as benign, malignant or normal. A clinician confirms every result.
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