Our lab has secured $20,000 from Google.org and $15,000 in support from OpenAI to accelerate our work
Read MoreResearch Project
Evaluating whether multimodal AI systems can convincingly add disease findings to normal radiological images - and releasing a curated, radiologist-scored synthetic dataset for benchmarking.
Leads: Dr. Shreyas Reddy & Sarath Sasi (CRASH Lab, Ashoka). CRASH Lab lead: Dr. Suvrankar Datta.
Status: Active - Supported by Google.org through Gemini Academic Program.
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 →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.
Active - Self-Funded
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