Send us a textAI in Pathology: ML-Ops and the Future of DiagnosticsWhat if the most advanced AI models we’re building today are doomed to die in the machine learning graveyard? 🤯 That’s the haunting question I tackled in the final episode of our 7-part series exploring the Modern Pathology AI publications.In this session, I explored machine learning operations (ML-Ops)—what they mean for digital pathology —and why even the most brilliant algorithm can fail without proper deployment strategies, data infrastructure, and lifecycle management.But we don’t stop there. I take you on a future-forward tour through multi-agent frameworks, edge computing, AI deployment strategies, and even virtual/augmented reality for medical education. This isn’t sci-fi. This is happening now, and as pathology professionals, we need to be prepared.🔗 Full episode reference: Modern Pathology - Article 7: AI in Pathology ML-Ops and the Future of Diagnostics Read the paper🔍 Episode Highlights & Timestamps[00:00] – Tech check, community shout-outs, and livestream reflections [02:00] – Overview of ML-Ops: What it is and why pathologists should care [03:45] – What’s a Machine Learning Graveyard? Personal examples of models I’ve built that went nowhere [05:30] – Machine learning platforms: from QPath to commercial image analysis tools [06:45] – The lifecycle of ML models: Development, deployment, and monitoring [09:00] – Mayo Clinic and Techcyte partnership: Real-world deployment integration [12:30] – Frameworks & DevOps tools: Docker, Git, version control, metadata mapping [14:30] – Model cards in pathology: Structuring ML model metadata [16:30] – Deployment strategies: On-premise, cloud, and edge computing [20:00] – PromanA and QA via edge computing: Doing quality assurance during scanning [23:00] – Measuring ROI: From patient outcomes to institutional investment [25:00] – Multi-agent frameworks: AI agents collaborating in real-time [28:00] – Narrow AI vs. General AI and orchestrating narrow tools [30:00] – Real-world applications: Diagnosis generation via AI collaboration [32:00] – Virtual & Augmented Reality in pathology training: From smearing to surgical simulation [35:00] – AI in drug discovery and virtual patient interviews [38:00] – Scholarly research with LLMs: Structuring research ideas from unstructured data [41:00] – Regulatory considerations: Recap of episode 5 for frameworks and guidelines [42:00] – Recap and future updates: Book announcements, giveaways, and next stepsResource from this episode🔗 Modern Pathology Article #7: AI in Pathology ML-Ops and the Future of Diagnostics🛠️ Tools/References mentioned:QPath (Free Image Analysis Tool)Techcyte & Aiforia for model development and deploymentPromanA for edge computing and real-time QAModel Cards (Pathology-specific metadata structure)Apple Vision Pro, Meta Oculus, HoloLens for VR/AR learningDr. Hamid Ouiti Podcast on software failure in medicineDr. Candice Chu's AI-assisted academic writing frameSupport the showGet the "Digital Pathology 101" FREE E-book and join us!

Ver todos los episodios