Our lab has secured $20,000 from Google.org and $15,000 in support from OpenAI to accelerate our work
Read MoreResearch Project
A national initiative - with MSRI and ICMR - to evaluate and benchmark ambient AI systems that convert spoken clinical encounters into structured, usable health data, built for India's digital health ecosystem.
Leads: Prof. Anurag Agrawal, Ashish Makani & Dr. Suvrankar Datta (KCDH-A); Kalika Bali, Sunayana Sitaram, Mohit Jain (Microsoft Research, India). CRASH Lab lead: Dr. Suvrankar Datta, Akash Ghosh.
Status: Active - Gates Foundation.
RadLE - Can Today's Leading AI Models Read Medical Scans Like a Radiologist?
An ongoing study comparing how the newest reasoning AI models interpret radiology scans against practicing radiologists - and how confident the AI is in its own readings.
Active - Simons Foundation
Creating the first standardised classification of the visual reasoning mistakes AI models make on radiology images - so they can be spotted, measured, and fixed.
Active - Simons Foundation
View research →A globally diverse radiology dataset with structured reasoning labels to train AI models that can reason through diagnoses step-by-step. CRASH Lab leads the Indian consortium for this Stanford-led Global Radiology Consortium.
Active - Global Radiology Consortium
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