AI-Enhanced Handheld ECGs to Screen for Prior Myocardial Infarction in Rural India

Efforts to move care out of hospitals and into the community face a hard constraint: while most medications can be delivered anywhere, diagnosis is still mostly trapped inside the formal health system-clinics, hospitals, and the people and machines inside them.This is especially binding in low-and middle-income countries due to distance, cost, and limited health system capacity.Lowcost data collection devices (e.g., handheld electrocardiograms (ECGs), retinal cameras) are promising, but do not solve the bottleneck: their outputs still require expert interpretation.Artificial intelligence (AI) could turn these raw signals into actionable outputs at scale, but most medical AI is trained in high-income settings and deteriorates in different populations and settings.We assemble a new dataset in rural Tamil Nadu, India that links low-cost device signals to hospital-grade reference testing.We use it to build a proof-of-concept screening tool: an algorithm that predicts the result of a hospital-based reference-standard test (regional wall motion abnormality on echocardiography), using only data from a handheld ECG device (cost: $60).This would allow targeting of secondary prevention medications-high-dose statins, antiplatelet agents, and beta blockers-proven to be cost-effective in both high-and low-resource settings.We evaluate predictive performance and cost-effectiveness of such screening, benchmarking against common clinical risk scores (including the American Heart Association 10-year risk score).In a held-out test set, our model identifies subgroups with rates high enough to make screening very cost-effective: the top 2.5% have a 9.3% rate of evidence consistent with prior heart attack-about six times the base rate-delivering benefits at US$1,999 per life-year saved.The model also flags highyield patients who would be missed by standard clinical scores, underscoring the limits of transporting Western-derived risk tools to low-and middle-income settings.To enable extensions to other conditions in the dataset, we make all data freely available for research.