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AI in Pathology: ML-Ops and the Future of Diagnostics

What 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 steps

Resource 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 deployment
  • PromanA for edge computing and real-time QA
  • Model Cards (Pathology-specific metadata structure)
  • Apple Vision Pro, Meta Oculus, HoloLens for VR/AR learning
  • Dr. Hamid Ouiti Podcast on software failure in medicine
  • Dr. Candice Chu's AI-assisted academic writing frame

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