Not super surprising for AI models, especially for BrainMRI since the modality itself is super heterogeneous. However for practitioners, it’s interesting…

seems like it as of now

🚀 Worried about faculty openings? Ohio State @OhioState is to hire 100 new faculty with AI expertise over the next five years! 🤖🎓 The new hires will join one of three AI Faculty Cohorts: 🧠 Foundational AI — Elevating the theoretical, mathematical, and algorithmic

We are currently designing a refreshed syllabus for the upcoming 2026 version of our @Stanford CS342 Building for Digital Health course which incorporates "structured" vibe coding! @DrAalami @StanfordBDHG

Cool to see the figure from my paper earlier is still up on the cover! Thanks @Radiology_AI @BenjaminKannMD

Are you driven to use AI to transform patient outcomes in oncology? My lab in the AI in Medicine Program (Mass General Brigham, Harvard Medical School) is seeking Postdoc Fellows to pioneer applications of AI—especially LLMs—in cancer care. More here:

A new study introduces a self-supervised deep learning method, temporal learning, for longitudinal medical imaging, showing improved recurrence prediction in pediatric gliomas with more historical scans. Full study results: https://t.co/qdZgf45srb

No surprise: Getting the historical brain scans improves prediction tham just the latest scan to predict recurrent in brain cancer. Time for us to upgrade our clinical interpretative pipelines to help our doctors and patients @nejm_ai https://t.co/kvDjeOZ0xd

How can we better predict if a child’s brain tumor will return—before symptoms start? In our new study @NEJM_AI, we used deep learning to analyze longitudinal brain MRIs—learning how scans change over time—to predict tumor recurrence in children. Let’s dive in. 🧠📊 👇 🧵1/