Radiology, pathology, dental — clinical-grade IAA (inter-annotator agreement) is the bottleneck, not raw throughput.
Patients call in 8+ languages. Triage and appointment booking can't stall on language barriers.
Insurance pre-auth, post-discharge claim packs, and EMR write-back are 60% of the operational load — and the most thankless.
Indian hospitals targeting ABDM linkage; export-facing operators need HIPAA. The data plane has to support both.
Radiology (CT/MRI/X-ray), pathology (whole-slide imaging), dental, dermatology. Multi-rater consensus, clinical-reviewer QA, RAGAS-style faithfulness checks.
Multilingual triage, appointment booking, post-discharge follow-up. Hindi + 11 Indian languages. Per-resolved-call commercials.
OCR + LLM extraction on insurance forms, automatic pre-auth packs, denial-pattern analytics.
ABDM-ready patient APIs, HL7/FHIR connectors for hospital EMRs, deduplicated patient identity across facilities.
Multi-rater pipeline for chest CT and MRI knee studies. 98.4% IAA, 12K studies / week with two clinical reviewers in the loop.
Hybrid AI + human triage call center across 6 languages. Same headcount, 7× call throughput on appointment booking.
Yes. By default, we de-identify before any LLM call. Patient identifiers stay in your VPC, only redacted text or pseudonymised data hits the inference layer. We can also operate fully on-prem for hospitals that prefer it.
Radiology (CT, MRI, X-ray, ultrasound), pathology (WSI), dental, dermatology, ophthalmology. We also do clinical-text NLP — discharge summaries, prescriptions, OPD notes.
Patient-discovery and care-context APIs, consent-manager linkage, and an FHIR-shaped data model. We've built this end-to-end and can do the integration in 4–6 weeks.