2025
12:00 PM - 01:00 PM
Tuesday Tue
Location
Medical Physics Seminar - Atilla Kiraly
Time:
12:00pm – 1:00pm Seminar & Discussion
Location:
Zoom Webinar
Webinar Registration:
https://stanford.zoom.us/webinar/register/WN_7SD49gRuS4iMtDPlP9IEGw
Check your email for the Zoom webinar link after you have registered
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Dr Atilla Kiraly, Software Engineer, Google Health
Atilla has over 20 years of experience in healthcare research and innovation. As part of Health AI at Google Research, he developed state-of-the-art lung and breast cancer detection systems deployed internationally through healthcare partnerships. Currently, his focus is on creating generalizable foundational models to accelerate healthcare discovery and innovation. Prior to Google, Atilla contributed to startups and medical imaging companies, developing novel technologies for radiology and interventional radiology, earning an R&D 100 Award. He has authored over 100 publications and patents and co-authored an ISO standard on AI bias. He holds PhD and MS degrees in Computer Science and Electrical Engineering, specializing in medical imaging, from The Pennsylvania State University. Atilla is passionate about AI’s potential to revolutionize healthcare.
Abstract
The potential of artificial intelligence (AI) in healthcare has been limited by the breadth of the applications required for transformative impact and high cost of building task-specific models, due to data and compute requirements. Recent work unlocks the potential of new capabilities and applications including zero-shot classification, multimodal classification, semantic search, visual question answering, and automated radiology report generation. To accelerate the adoption of AI in medicine, we created Health AI Developer Foundations (HAI-DEF), a suite of foundational models spanning diverse medical modalities, including computed tomography, pathology, dermatology, chest X-ray, and bioacoustic data. HAI-DEF models, trained on large datasets, can significantly reduce the data and computational resources required for downstream AI development. These models can be used to advance novel research investigations and medical device development by setting a strong baseline in performance that can be further improved upon by researchers and developers. This work provides a critical step towards more accessible, powerful, and versatile AI solutions in healthcare.