Our lab has secured $20,000 from Google.org and $15,000 in support from OpenAI to accelerate our work
Read MoreResearch Project
Creating the first standardised classification of the visual reasoning mistakes AI models make on radiology images - so they can be spotted, measured, and fixed.
Leads: Dr. Divya Buchireddygari (CRASH Lab). CRASH Lab lead: Dr. Suvrankar Datta, Snigdha.
Status: Active - Simons 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
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
View research →Evaluating whether multimodal AI systems can convincingly add disease findings to normal radiological images - and releasing a curated, radiologist-scored synthetic dataset for benchmarking.
Active - Google.org Gemini Academic Program
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