MedRLM: Recursive Multimodal Health Intelligence for Long-Context Clinical Reasoning, Sensor-Guided Screening, Evidence-Grounded Decision Support, and Community-to-Tertiary Referral Optimization
AI that reasons through a patient's complete medical history to guide treatment decisions
Most medical AI answers isolated questions quickly but struggles when the real answer requires connecting facts scattered across patient records, images, and sensor data. MedRLM instead builds a dynamic "evidence map" that recursively searches through a patient's full medical picture—text notes, imaging, heart rhythms, blood pressure trends, and clinical guidelines—activating deeper analysis when abnormal patterns appear, then flags cases for human review when confidence is low.
Healthcare providers in rural or under-resourced areas often lack specialists to review complex cases. A system that can systematically extract and connect evidence across all available patient data, then decide whether a case needs referral to a tertiary hospital, could reduce delays in care and improve triage accuracy. The framework's built-in uncertainty checking also prevents overconfident recommendations that might lead clinicians astray.