Continuation of the PaJR Workflow ProJR from the previous link given below 👇
BODH: Benchmarking Open Data Platform for India Health AI - A Review of Architecture, Evaluation Methodology, and Implementation Framework for Clinical AI Validation in India
Prasanna Kumar C S
Vol. 15 No. 2 (2026): Volume XV Issue II February 2026 / Articles
GITAM University, Novotech Health Holdings, Bengaluru
DOI: https://doi.org/10.51583/IJLTEMAS.2026.15020000117 (https://doi.org/10.51583/
IJLTEMAS.2026.15020000117)
Published: Mar 21, 2026
Abstract How to Cite Metrics References License
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 de�cit.
Objective: To present the technical architecture, evaluation methodology, governance
framework, and anticipated clinical impact of BODH as India's �rst 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 signi�cant 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 veri�able 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.
How to Cite
BODH: Benchmarking Open Data Platform for India Health AI — A Review of Architecture,
Evaluation Methodology, and Implementation Framework for Clinical AI Validation in India.
(2026). International Journal of Latest Technology in Engineering Management & Applied Science,
15(2), 1324-1334. https://doi.org/10.51583/IJLTEMAS.2026.15020000117 (https://
Full Text HTM (https://www.ijltemas.in/submission/online/article/
view/4224/5701)
[6:11 pm, 24/03/2026] hu5: No actually, missed them there.. looks interesting
[6:11 pm, 24/03/2026] hu1: Let's have a look. Thanks for sharing.
[6:11 pm, 24/03/2026] hu1: Yeah agreed. Any more potential additions? It's actually quite a hassle to set these up 😅
[6:11 pm, 24/03/2026] hu2: The two below can be removed?👇
Name of Consent Taker
(People need not be giving consent to any individual and also for those who are directly engaging online without any intermediary this would be redundant)
PaJR Health ID
(No point showing this option when it's not yet made and once made the system would inform them automatically?)
[6:11 pm, 24/03/2026] hu1: Alright. So backend stuff can be taken off. Yes I guess this was carried over from the paper form
[11:44 pm, 24/03/2026] hu28: hu5, can you access the full text of this. And more so, are you able to actually access the portal for BODH? I am unable to see that from USA.
[11:44 pm, 24/03/2026] hu28: It should be available somewhere on ABDM
[9:56 am, 25/03/2026] hu2: There's a full text link in that PDF
Today's CPC rephrased:
This is a celebration of medicine learning by alumni from an urban hospital in India who are currently spread out globally in various urban and remote locations in the world and at one such rural location there is a current admitted patient being compared in parallel with the patient who died months ago in the same urban hospital as both appear to have overlapping data points. This comparison is happening asynchronously across a global web based platform even as the urban hospital patient's autopsy findings are being discussed offline synchronously around the same time on a Wednesday morning.
The integration of an AI bot into the discussion serves to structure and elevate the human reasoning process. By organizing clinical thoughts into an IMRAD format and posing Socratic questions, the AI acts as an ambient analytical partner. It bridges the temporal gap between the 1.5-year illness narrative of the deceased patient and the acute presentation of the current patient, synthesizing disparate data points to warn against diagnostic pitfalls (e.g., misinterpreting an autoimmune storm as chronic infection like TB).
No comments:
Post a Comment