Our lab has secured $20,000 from Google.org and $15,000 in support from OpenAI to accelerate our work
Read MoreResearch Project
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.
Leads: Sonali Sharma (Stanford University). CRASH Lab lead: Dr. Suvrankar Datta; Dr. Shreyas Reddy; Dr. Suyash Gunjal - Indian Consortium Leads.
Status: Active - Global Radiology Consortium.
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 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.
Active - Gates Foundation
View research →