ARI Health provides a smartphone‑based platform that creates a continuous digital twin of each pregnant patient, using causal AI to monitor physiological changes in real time. An augmented reality interface visualizes risk signals and the system applies deterministic safety protocols to deliver etiology‑specific alerts and care recommendations, enabling obstetricians and midwives to intervene weeks before complications arise while reducing manual chart review.
Funding
Funding not disclosed
Founders
Product
Problem
Current obstetric care relies on intermittent prenatal visits and manual chart reviews, which often miss early physiological changes that precede serious complications. Delayed detection of these silent precursors contributes to high rates of preventable preterm births and severe maternal morbidity.
Solution
ARI Health creates a continuous, smartphone‑based digital twin of each pregnant patient, using causal AI to monitor physiological drift in real time. An augmented reality interface visualizes the patient’s changing health status, making subtle risk signals visible to both the patient and the care team. The platform continuously applies deterministic safety protocols to identify the underlying etiology of emerging risks, delivering precise, etiology‑based recommendations without adding to clinicians’ administrative workload. By acting as an always‑on resident, the system flags potential decompensation weeks before it becomes a clinical emergency, enabling proactive, predictive interventions that improve outcomes and reduce costly complications.
Target Audience
Primary customers are obstetricians, midwives, and health systems that provide prenatal care, as well as pregnant women who use a smartphone for continuous health monitoring.
Features
- Real‑time digital twin generation from smartphone sensor data to track maternal physiology continuously
- Causal AI engine that detects subtle physiological drifts and isolates the underlying cause of risk
- Augmented reality visualization that presents health trends in an intuitive, patient‑friendly format
- Automated, protocol‑driven alerts and etiology‑specific care recommendations delivered to clinicians
- Reduction of manual chart review and interview time by continuously analyzing data in the background
- Integration-ready API for embedding alerts and reports into existing electronic health record systems