Research 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.
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
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
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
View research →