A protocol paper describes ASHA Assist India, a cloud-based AI mobile health platform designed to integrate stroke prevention workflows across India's rural healthcare hierarchy — connecting citizens, ASHA frontline workers, Primary Health Centres, and referral hospitals. The system proposes three AI modules: population-level stroke risk stratification, longitudinal risk prediction, and acute stroke symptom recognition, built atop India's Ayushman Bharat Digital Mission digital infrastructure.
Stroke kills roughly 700,000 Indians annually, and rural populations face compounded disadvantages: delayed risk identification, paper-based workflows, and broken referral chains. ASHA workers — India's 1 million-strong community health backbone — are theoretically ideal vectors for prevention, but are chronically overburdened and digitally underserved. Platforms like this could meaningfully shift stroke outcomes if implementation succeeds at scale.
However, this paper describes only a protocol, not results. No AI model performance data, usability metrics, or clinical outcomes are yet reported. The prospective evaluation remains planned rather than completed, and AI module validation awaits prospectively collected longitudinal data — a process that typically takes years. Critical questions about algorithmic bias in rural Indian populations, device accessibility, and ASHA worker adoption rates remain entirely unanswered.
As a preprint not yet peer-reviewed, even the protocol design itself may undergo revision. This represents an early-stage infrastructure proposal with genuine public health ambition, but readers should treat it as a research roadmap, not an evidence-based intervention. Incremental in novelty; potentially significant in impact if fully executed.
