BODH: Benchmarking Open Data Platform for India Health AI — A Review of Architecture, Evaluation Methodology, and Implementation Framework for Clinical AI Validation in India
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Background: India's healthcare AI landscape is rapidly evolving, yet a critical infrastructure gap persists: the absence of a sovereign, interoperable benchmarking platform for systematic validation of AI models against clinically representative datasets. This paper introduces BODH (Benchmarking Open Data Platform for Health AI), a pioneering digital ecosystem unveiled at the India AI Impact Summit 2026, designed to address this deficit.
Objective: To present the technical architecture, evaluation methodology, governance framework, and anticipated clinical impact of BODH as India's first federated AI benchmarking infrastructure for healthcare, conforming to international standards including HL7 FHIR R4, SNOMED CT, and OMOP CDM.
Methods: BODH employs a multi-layer microservices architecture incorporating federated data ingestion, a secure model evaluation sandbox, and a cryptographically audited leaderboard. Evaluation dimensions span diagnostic accuracy, fairness across demographic strata, model explainability (SHAP, LIME, integrated gradients), clinical safety, and regulatory alignment. Benchmark datasets cover radiology (chest X-ray, CT, MRI), pathology, genomics, clinical NLP (EHR), and wearable biosignals.
Results: Preliminary validation with 12 pilot AI models across 5 Indian hospital networks demonstrates that BODH's multi-dimensional scoring reduces overestimation of model accuracy by 18-34% compared to single-metric evaluation. Fairness gap indices reveal statistically significant performance disparities (p < 0.01) across gender and socioeconomic strata in 7 of 12 models, previously unreported in vendor evaluations.
Conclusions: BODH represents a transformational step in responsible AI adoption in Indian healthcare. By institutionalising open, reproducible, and regulation-aligned benchmarking, it creates a verifiable trust layer that bridges the gap between AI development and clinical deployment, serving as a model for low- and middle- income country (LMIC) AI governance frameworks.
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