Scaling Laws for Radiology Foundation Models with Max Ilse
Impossible d'ajouter des articles
Échec de l’élimination de la liste d'envies.
Impossible de suivre le podcast
Impossible de ne plus suivre le podcast
Scaling Laws for Radiology Foundation Models with Max Ilse
-
Lu par :
-
De :
À propos de ce contenu audio
In this first episode of Pixels 2 Patients, I’m joined by Max Ilse, Senior Researcher at Microsoft Health Futures, to discuss his latest paper, Data Scaling Laws for Radiology Foundation Models (arXiv:2509.12818).
We dive into the core findings of his research, exploring how scaling laws, long studied in natural language processing and vision, apply to the medical imaging domain. We talk about the trade-offs between building large, generalist models that perform across many tasks versus developing highly specialised models that excel within a particular population or modality.
Max also shares his thoughts on the current bottlenecks facing radiology foundation models, underexplored areas of research in the field, and what he sees as the next frontiers in scaling and deploying AI systems for healthcare.
If you’re interested in where medical imaging AI is heading — and what it takes to move from scaling experiments to clinical impact — this is a conversation you won’t want to miss.
Vous êtes membre Amazon Prime ?
Bénéficiez automatiquement de 2 livres audio offerts.Bonne écoute !
