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 ฮ25bp 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 sequentially on foundational ICMR guidelines (Phase 1) and then on a rigorously curated dataset of real Indian clinical notes and Gemini-synthesized phenotype shifts (Phase 2).
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.ekacare clinical notes, synthetic South Asian phenotype shifts, and digitized ECG mocks to deeply embed Indian cardiac contexts.Through a custom architectural patch bypassing the complex Gemma3 VLM structure, we successfully converted the fine-tuned model into a highly compressed Q4_K_M GGUF format (16.6GB). Cardio-Sahayak India is fully capable of running completely offline on standard clinical laptops in resource-constrained rural clinics.
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.