Kinila AI Health helps ASHA workers, ANMs, and community health volunteers assess patient symptoms accurately and flag emergencies — even in low-resource settings across Karnataka.
A two-step workflow designed for busy community health workers — no medical degree required.
Guides the health worker through patient age, gender, current medications, and known conditions before symptom entry — so nothing clinically important is missed.
Returns a clear Green / Yellow / Orange / Red triage level with plain-language guidance — monitor, refer, or escalate urgently. Designed to support, not replace, the CHW's judgement.
Validated at 97.1% sensitivity across 8 critical emergency types including febrile seizure, acute MI, neonatal sepsis, pre-eclampsia, and hypertensive crisis. No emergency was missed.
Links each assessment to a site, institution, and session reference — making it easy to audit cases, follow up, and report to programme supervisors.
When uncertain, the tool errs toward caution — over-referring rather than under-triaging. 99% of all outputs in the validation study were rated safe or borderline-safe.
Designed with and for ASHA workers, ANMs, and community health volunteers — not hospital clinicians. No prior clinical training required to use the tool effectively.
Kinila AI Health is a decision-support tool for workers providing care in communities where specialist access is limited or delayed.
Accredited Social Health Activists conducting household-level health assessments and referrals in rural and peri-urban communities.
Auxiliary Nurse Midwives and Community Health Officers at sub-centre and PHC level managing a wide range of clinical presentations.
District health officers, programme managers, and NGO partners looking to support CHW decision-making with a validated AI tool.
Institutions seeking evidence-based AI tools for community health in South Asian primary care settings — with a clear validation pathway and funded evaluation in progress.
A structured validation study conducted in March 2026 using standardised clinical case scenarios reflecting PHC-level South Indian presentations across fever, GI, respiratory, obstetric, NCD, and paediatric categories.
20 standardised clinical vignettes · 90 paired AI and student assessments · 4–5 participant groups · Conducted at a tertiary care institution in coastal Karnataka as part of the EVAH Pathway A application.
All 8 RED emergency types — febrile seizure, acute MI (known and unknown history), severe dehydration, pre-eclampsia, neonatal sepsis, severe diarrhoea, and hypertensive crisis — correctly identified across every participant group.
All errors are over-cautious (upward triage), never dangerous under-triage of emergencies. The 4 ORANGE under-triage cases are specific, addressable adaptation targets: TB screen, hypoglycaemia, PID, and diabetic foot.
The funded evaluation will deploy across 39 PHCs at partner institutions in coastal Karnataka, with actual CHWs as primary users and native language support integrated.
The validation study was conducted at a medical institution in coastal Karnataka — a pre-deployment evaluation using standardised clinical case scenarios reflecting primary health centre presentations in South India. The evaluation will extend to more PHCs across partner institutions in the region.
Kinila AI Health is a product of Kinila Ventures, a partnership firm based in Udupi, Karnataka, working at the intersection of community health, life sciences, and AI.
Building tools and solutions at the intersection of community health, life sciences consultation, and applied AI for underserved populations in India.
kinilaventures.com →Expert consultation for biotech and pharmaceutical organisations navigating research, regulatory, and clinical development challenges in India and globally.
A community-facing knowledge and library initiative connecting people in the Udupi region with learning resources, civic knowledge, and public information.
Whether you are a community health worker ready to use the tool, a health official evaluating it for your programme, or a funder interested in the evidence — we would like to hear from you.