233: AI-Driven Breast Cancer Staging in Resource-Constrained Settings

About this episode

The episode discusses the potential of AI to predict breast cancer stages in settings with limited resources.

Send us Fan Mail Paper Discussed in this Episode: Deep-learning-based breast cancer stage prediction from H&E-stained whole-slide images in resource-constrained settings. Bedőházi Z, Biricz A, Kilim O, et al. Journal of Pathology Informatics 21 (2026) 100644. Episode Summary: Welcome back, Trailblazers! In this Journal Club deep dive of the Digital Pathology Podcast, we flip the core assumption of microscopic precision on its head. Can an AI accurately predict pathological breast cancer s...

People in this episode

Host: Aleksandra Zuraw

Topics covered

• AI in healthcare
• breast cancer staging
• resource-constrained settings
• digital pathology
• deep learning

Keywords

• AI
• breast cancer
• staging
• deep learning
• pathology
• resource-constrained

Mentioned in this episode

Organizations: Journal of Pathology Informatics

Books & works: Deep-learning-based breast cancer stage prediction from H&E-stained whole-slide images in resource-constrained settings

More episodes of Digital Pathology Podcast

• 239: Can AI Copilots Keep Up with Pathologists? · June 3, 2026 · 33 min
• 238: How Do We Know AI Is Ready for Pathology · May 19, 2026 · 20 min
• 237: Why Pathology Vendor's Don't Speak the Same Language? · May 18, 2026 · 33 min
• 236: What Happens When a Patient Sees Their Cancer for the First Time | Podcast with Michele Mitchell · May 15, 2026 · 1h 13m
• 235: From Cytology to Omics: Where Pathology AI Gets Harder · May 12, 2026 · 33 min
• 234: Quality, Teaching, and AI: A Practical Shift in Pathology · April 25, 2026 · 36 min