Our lab has secured $20,000 from Google.org and $15,000 in support from OpenAI to accelerate our work
Read MoreResearch Project
Creating scalable autonomous pipelines to learn how individual radiologists write their reports, so that AI-generated radiology reports preserve each doctor's reporting voice while staying medically accurate.
Leads: Dr. Suvrankar Datta (CRASH Lab, KCDHA). CRASH Lab lead: Siddharth Reddy, Haritha R, Dr. Nishtha Mahajan.
Status: Active - Self-Funded.
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 →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 →