Optimized for the South Asian demographic, addressing unique genetic predispositions, clinical profiles, and socio-economic contexts to democratize advanced cardiac care.
Myocardial Infarction (MI) in India occurs 5-10 years earlier than in Western populations. Despite standard BMIs, central obesity, genetic predispositions (like the MYBPC3 Δ25bp variant), and unique metabolic profiles present a distinct "South Asian Phenotype" often overlooked by generalized medical AI models.
Current models fail to account for lower BMI thresholds for MI, elevated Lp(a) levels, and specific variants prevalent in up to 4% of the Indian population.
Combining MedGemma-27B for deep clinical reasoning and a Vision-Language Model (MedSigLIP) for native 12-lead ECG interpretation.
Fine-tuned directly on the Indian National Consensus on Cardiology and ICMR guidelines for precise, actionable diagnostic support.
Cardio-Sahayak implements a state-of-the-art dual-architecture design:
google/medgemma-27b-it, quantized using 4-bit NormalFloat (NF4) via bitsandbytes and fine-tuned using QLoRA.google/medsiglip-448 integrated via the MEIT framework, allowing raw 12-lead ECG ingestion and cross-modal diagnostic reasoning.By converting the fine-tuned model to GGUF format, Cardio-Sahayak India is designed to be deployed locally on resource-constrained hardware in rural Indian clinics. It acts as an expert clinical assistant (Sahayak), assisting physicians in early screening, accurate ECG interpretation, and personalized risk assessment.
Released to the community to foster collaboration, rigorous evaluation, and advancements in global health equity.
A specialized solution for the 1.4 billion people of India, directly addressing the region's rapidly growing burden of cardiovascular disease.