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 it’s been such a catalyst for 3D vision–language research, and what it enables for training foundation-style models in radiology. The dataset is publicly available on Hugging Face here: https://huggingface.co/datasets/ibrahimhamamci/CT-RATE

If you want a clear primer on core concepts in AI for radiology—representation learning, multimodal supervision and what changes when you go from 2D images to 3D volumes—this conversation is a great starting point. We also dig into why a lot of work still needs to be done: models need to be more robust across scanners and sites, evaluation has to go beyond headline metrics, and real clinical use demands reliability, workflow fit, and accountability.