Cardia-AI: Passive Cardiac Event Monitoring Using Smartwatch Sensors and Predictive Analysis via Large Language Models

Cardiovascular diseases require continuous, context-aware monitoring, i.e., combining day-to-day wearable signals with recent diagnoses, medications, and symptom reports, rather than isolated clinic visits or single-spot measurements. We developed Cardia-AI, a proof-of-concept pipeline that time-aligns smartwatch signals (heart rate, blood pressure, oxygen saturation) with a patient's longitudinal electronic health record (EHR) and uses a compact medical language model with retrieval to produce grounded educational summaries. Cardia-AI assembles time-aligned summaries through a select, compile, and ask pipeline, and uses a lightweight medical large language model (BioMistral-7B) with retrieval from curated sources. Guardrails constrain outputs to education and navigation, and the system incorporates explicit escalation guidance when red-flag symptoms are present in the prompt context. In two scenario-based validations (cardiometabolic education; early post-angioplasty recovery), Cardia-AI compiled synchronized smartwatch trends with EHR entries, referenced the exact measurements and diagnoses present in the prompt, and recorded transcripts for audit and reproducibility; no outcomes or accuracy endpoints were assessed. We did not evaluate clinical effectiveness or diagnostic accuracy; no patient outcomes were measured. This work reports feasibility and safety guardrails only, with prospective evaluations planned. These demonstrations suggest that pairing wearable streams with a compact, domain-tuned language model may lower cognitive load from multi-panel charts and shorten the path from symptom onset to appropriate follow-up under clinician oversight.