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“AI in Pathology Isn’t Coming — It’s Already Here. Are You Ready?”

From confusion to clarity — that’s what this episode is all about. I sat down with Drs. Liron Pantanowitz, Hooman Rashidi, and Matthew Hanna to dissect one of the most important and comprehensive AI-in-pathology resources ever created: the 7-part Modern Pathology series from UPMC’s Computational Pathology & AI Center of Excellence (CPAiCE). This isn’t just another opinion piece — it's your complete guide to understanding, implementing, and navigating AI in pathology with real-world insights and a global lens.

Together, we discuss:

• Why pathologists and computer scientists are often lost in translation
  
  
• How AI bias, regulation, and ethics are being addressed — globally
  
  
• What it really takes to operationalize AI in patient care today
  
  

If you’ve ever asked, “Where do I even start with AI in pathology?” — this is your answer.

🔍 Highlights & Timestamps
00:00 – The importance of earned trust in AI
01:00 – Education gaps in AI for both pathologists & developers
03:00 – Why CPAiCE was built & the three missions it serves
07:00 – The seven-part series: a blueprint for AI literacy
10:00 – Making AI education accessible without losing technical integrity
13:00 – How this series is being used for global teaching (including by me!)
17:00 – Generative AI in creating figures vs. human-authored content
21:00 – Eye-opening global AI regulations that pathologists MUST know
24:00 – Ethics, bias & strategies to mitigate real clinical risks
30:00 – What’s next: CPAiCE’s mission to reshape pathology education & practice
34:00 – A teaser: the first CPAiCE textbook is on the way!

📚 Resources from This Episode

📰 Read the full series (open access!):
Modern Pathology 7-Part AI Series: https://www.modernpathology.org/article/S0893-3952(25)00001-8/fulltext

👨‍⚕️ UPMC’s Computational Pathology & AI Center of Excellence (CPAiCE)
🌍 Creative Commons licensing means YOU can reuse, remix & teach from these resources — just cite the source.

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You think Saturday mornings are for coffee? Try diving into bone marrow morphology, organ donor kidney biopsies, and AI-driven metastasis detection at sunrise. That’s how I do it—and you’re invited to join.

Welcome to another data-packed episode of DigiPath Digest, where we explore the latest frontier in digital pathology and AI. This time, I reviewed some of the most exciting recent abstracts spanning cancer grading, T-cell quantification, and AI agents in oncology decision-making.

These studies aren’t just fascinating—they’re redefining what’s possible in diagnostics, especially in under-resourced areas where digital pathology can create game-changing access and efficiency.

🔬 Highlights with Timestamps

[00:04:00] Detecting Metastases with Vision Transformers
A team from Leeds Teaching Hospital developed a model for identifying lymph node and omental metastases in ovarian and peritoneal cancers with 99.8% AUROC and 100% balanced accuracy—this isn’t hype; it’s real AI pre-screening that could reduce diagnostic strain on pathologists.

[00:08:00] DeepHeme: Bone Marrow Smears Meet AI
UCSF and Memorial Sloan Kettering collaborated on DeepHeme, an ensemble deep learning model that classifies bone marrow aspirate cells with expert-level accuracy. With over 30K training images and strong external validation, it outperforms humans in both speed and detail.

[00:16:00] Multimodal AI for Head & Neck Cancer
This review showcases how integrating radiology, histopathology, and genomics with AI enhances personalized treatment and prognosis. Spoiler alert: Multimodal > unimodal.

[00:24:00] Real-Time Kidney Biopsy Evaluation via AI
Shoutout to our Digital Pathology Place sponsor, Techcyte, for their AI-powered tool improving accuracy and halving the time it takes to evaluate frozen kidney biopsies. This is the kind of innovation we need in organ transplantation.

[00:32:00] GPT-4 as an Oncology Agent?
Heidelberg researchers created an autonomous AI agent using GPT-4 plus vision models and OncoKB to handle oncology case decisions with 91% accuracy. This isn’t ChatGPT guessing—it’s a hybrid system citing guidelines and performing complex reasoning.

🧠 Resources From This Episode

• 📰 Multiple Instance Learning for Metastases Detection in Ovarian Cancer – Cancers journal
• 🧬 DeepHeme: Generalizable Bone Marrow Cell Classifier – Science Translational Medicine
• 📚 AI in Head and Neck Cancer: A Multimodal Review – Cancers journal
• 🧪 AI-Assisted Review of Donor Kidney Pathology – Techcyte & Digital Pathology Place demo
• 🤖 Autonomous AI Agent for Oncology Decisions – Heidelberg Group
• 🎙️ Podcast on GPT-4 agents with Dr. Nina Kolker
• 🧵 Earrings mentioned in the livestream? Find them in the DPP Store

I’d love to hear your feedback, your projects, and what digital pathology means to you. You can always reach out through comments, LinkedIn, or email.

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Become a Digital Pathology Trailblazer get the "Digital Pathology 101" FREE E-book and join us!

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AI Pathology & Genomics: A New Benchmark for Predicting Gene Mutations

If you still think visual quantification is “good enough” in pathology, think again.
In this 27th episode of DigiPath Digest, I break down four transformative abstracts that show how AI is shifting our diagnostic landscape—from breast cancer segmentation to fibrosis assessment, and all the way to spatial immunology and the evolving immunoscore.

If you’re still relying on manual scoring, static staging systems, or single-marker immunohistochemistry, this episode will challenge you to look deeper—literally and algorithmically.

🔬 Episode Highlights & Timestamps

[02:00] Abstract 1 – AI + IHC for epithelial cell segmentation in breast cancer
[07:30] Abstract 2 – Deep learning quantifies TILs in esophageal cancer
[14:30] Abstract 3 – Biopsy size impacts SHGTPF-based liver fibrosis staging
[22:30] Abstract 4 – Immunoscore in colorectal cancer: promise & limits

🧬 Key Insights & Takeaways

1. IHC-Guided Segmentation for Breast Cancer
Using immunohistochemistry as a ground truth for AI segmentation reveals how effective our models can be—but also where they fall short. The challenge? Accurately subclassifying benign, in situ, and invasive epithelial cells. Spoiler: We’re not quite there yet.

2. Tumor-Infiltrating Lymphocytes in Esophageal SCC
A Chinese team trained deep learning algorithms to analyze TILs spatially. Result? High TIL counts in both intra- and peritumoral zones correlated with better survival—highlighting the emerging power of spatial immunology.

3. Liver Fibrosis Staging with SHGTPF Microscopy
Second harmonic generation two-photon microscopy gives us label-free imaging of unstained tissue. The takeaway: bigger biopsies (20–26mm) yield better fibrosis quantification. Biopsy position? Surprisingly irrelevant. A game-changer for MASLD diagnostics.

4. Immunoscore for Colorectal Cancer
This image analysis-based tool outperforms traditional TNM staging, helping stratify patients for immunotherapy. But adoption is hampered by cost and digital slide access. Integrating AI could take it to the next level—something we should all watch closely.

🎓 Resources from This Episode

• Breast cancer segmentation using IHC-guided AI (Trondheim, Norway)
• Esophageal SCC & spatial TILs (Cancer Medicine, China)
• SHGTPF microscopy in liver fibrosis (UK/US multi-center study)
• Immunoscore in colorectal cancer (Jerome Galon group origins)

💡 Bonus: I show off some histology-inspired earrings and talk about the story behind them—multinucleated giant cells, cartilage, and more. Check them out if you’re into pathology fashion!

We’re not just validating AI anymore—we're redefining diagnostics. From high-res, label-free imaging to robust spatial biology insights, the path forward in pathology is clearer and more precise than ever. Whether you’re a practicing pathologist, researcher, or innovator, this episode offers tools and perspectives you can apply today.

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Become a Digital Pathology Trailblazer get the "Digital Pathology 101" FREE E-book and join us!