Pixels 2 Patients

Science EN ↓ 3 episoder

Pixels 2 Patients goes behind the research shaping the future of AI in medical imaging and healthcare. Hosted by PhD Researcher Dhruv Gupta, each episode features in-depth conversations with the first authors and researchers pushing the boundaries of machine learning for radiology and clinical care. Together, we explore how their work came to life, the challenges they faced, and what their findings reveal about the limitations, opportunities, and next directions for medical imaging AI. From self-supervised learning to foundation models and real-world deployment, Pixels 2 Patients looks at how...

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Pixels 2 Patients

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Science

Podcastens hjemmeside

rss.com

Seneste episode

15. jan. 2026

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Episoder

Moving Beyond 2D: 3D CT AI with Ibrahim Ethem Hamamci 15.01.2026 1:02:15

In this episode, I sit down with Dr. Ibrahim Ethem Hamamci to unpack what it really takes to build modern AI for 3D CT—covering multimodal learning (images + text), the practical realities of clinical deployment, and why “moving beyond 2D” is such a big shift in radiology AI. We dive into CT-RATE, a large-scale dataset of chest CT volumes paired with radiology reports (plus labels/metadata), why i...

AI-Powered Education for Dr's with Mohammed Baharoon 19.11.2025 1:22:42

In this episode, I speak with Mohammed Baharoon , MMSc candidate in Biomedical Informatics at Harvard Medical School and researcher in the Rajpurkar Lab. We discuss his latest work at the intersection of radiology AI, dataset creation, and AI-powered medical education. Our conversation focuses on two of his recent projects: ReXGroundingCT , an open-source dataset that links free-text CT reports wi...

Scaling Laws for Radiology Foundation Models with Max Ilse 05.11.2025 54:34

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 abou...