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	Podcast:		Digital Pathology Podcast
	Episode:		147: Non-Generative AI – Predictive Analytics & ML – 7-Part Livestream 3/7
	Category:		Science & Medicine
	Duration:		00:44:20
	Publish Date:		2025-08-10 12:00:00
	Description:		Send us a text What if I told you the biggest AI breakthroughs in pathology aren’t coming from ChatGPT or generative tools—but from the quiet power of predictive analytics and machine learning? In this episode, I explore the non-generative side of artificial intelligence in pathology. These are the tools that detect tumors, segment tissue, classify images, and make predictions—without generating a single word. It’s the third chapter in our guided AI series, and this time we focus on the models you’re more likely to use in real-world diagnostics. You’ll hear about object detection, segmentation, anomaly detection, and how these models are built using supervised and unsupervised learning—plus the pros and cons of different annotation strategies. We’ll also cover why no one model fits all, and how combining simple tools like decision trees with more complex neural networks is often the key to building reliable, usable AI in pathology. Whether you’re training your first model, selecting an algorithm for rare disease detection, or just want to understand what “unsupervised clustering” means—you’ll find something useful here.
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