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Can one AI system learn from every organ — and teach us something new about all of them?

In this edition of DigiPath Digest #31, I explore how artificial intelligence is transforming pathology across multiple organ systems, revealing connections that help us diagnose faster, more consistently, and more accurately than ever before.

From glomerulonephritis to hepatocellular carcinoma, AI is no longer confined to a single specialty — it’s becoming the connective tissue between them.

What’s Inside:

1️⃣ AI for Bladder Cancer Classification
We begin with a multicenter study validating AI models for urothelial neoplasm classification using over 12,000 whole-slide images. Both CNNs and transformer models achieved high accuracy (AUC 0.983, F1 score 0.9). I discuss why the F1 score matters — and what it tells us about model balance between sensitivity and specificity.

2️⃣ AI in Colorectal Cancer Care
Next, we explore multimodal AI — integrating histopathology, radiology, genomics, and blood markers to modernize colorectal cancer workflows. AI now helps detect adenomas, infer microsatellite instability (MSI) from H&E slides, and predict treatment outcomes. I highlight the critical need for external validation, interpretability, and governance as AI enters clinical use.

3️⃣ AI for Glomerular Nephritis Diagnosis
A deep learning model trained on over 100,000 kidney biopsy images identified four nephritis types — FSGS, IgA, MN, and MCD — with over 85% accuracy. This technology could ease workloads and improve turnaround time in renal pathology. Still, I share why AI support may feel both empowering and unsettling for many pathologists.

4️⃣ AI in Liver Disease (MASLD & HCC)
AI is advancing noninvasive fibrosis staging and risk prediction in liver pathology. From large consortia like NIMBLE and LITMUS to predictive models for HCC therapy response, AI is moving us closer to precision hepatology. I also discuss the challenge of translating these tools from research to regulatory approval.

5️⃣ Lightweight AI for Domain Generalization
Finally, we look at one of pathology AI’s biggest challenges: domain shift — when a model trained on one scanner or staining style performs poorly elsewhere. The new Histolite framework shows how lightweight, self-supervised models can generalize across data sources — trading some accuracy for reliability in real-world use.

My Takeaway

Across every study, a single message stands out:
AI isn’t replacing pathologists — it’s amplifying our vision.
By connecting kidney, colon, liver, and bladder insights, AI is teaching us that medicine works best when it learns across boundaries.

Episode Highlights

• Bladder cancer AI validation (06:41)
• Multimodal colorectal AI (12:38)
• Glomerular nephritis deep learning (19:29)
• AI in liver pathology (29:55)
• Domain shift & Histolite framework (38:17)
• Halloween wrap-up + SITC preview (46:18)

Join me next time for updates from the SITC 2025 Conference, where I’ll be live at Booth 415 with Hamamatsu and Biocare, discussing how AI and spatial biology are converging to drive clinical utility.

#DigitalPathology #AIinHealthcare #ComputationalPathology #CancerDiagnostics #LiverPathology #RenalPathology #FutureOfMedicine #DigiPathDigest

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If artificial intelligence can match—or even surpass—our diagnostic accuracy, what happens to the role of the pathologist?

That’s the question I explore in this episode of DigiPath Digest #30, where I break down three fascinating papers showing how AI is changing the way we diagnose, classify, and predict outcomes in renal transplant biopsies, thyroid cytology, and gastrointestinal cancers.

These studies don’t just prove AI’s potential—they reveal what it means for us, the humans behind the microscope.

Study 1 — Renal Transplant Biopsies: Precision in Every Pixel

A Japanese team examined how deep neural networks and large language models improve diagnostic consistency in renal transplant pathology.

They highlighted how the Banff Digital Pathology Working Group is retraining AI models alongside updated Banff classifications—creating a dynamic feedback loop between human expertise and machine learning.

In the U.S., over ten digital pathology systems are now FDA-cleared for primary diagnosis, showing that AI can support both accuracy and accountability. It’s not replacing us—it’s working with us.

Study 2 — Thyroid Cytology: From Overdiagnosis to Optimization

As someone who’s personally experienced thyroid cancer, this study hit close to home.

Researchers in China developed AI-TFNA, a multimodal system that combines whole-slide images and BRAF mutation data from over 20,000 thyroid fine-needle aspirations across seven centers.

The model achieved 93% accuracy, reducing unnecessary surgeries and improving clinical decisions. What’s especially impressive is Image Appearance Migration (IAM)—a technique that helps AI adapt across scanners and labs, ensuring reliable performance worldwide.

Study 3 — GI Cancer: Prognosis Reimagined

An international collaboration of over 2,400 patients introduced a Deep Learning Pathomics Signature (DLPS) that merges nuclear features, tumor microenvironment, and spatial single-cell data.

This AI-driven model predicted patient survival and therapy response more accurately than traditional TNM staging—even identifying which patients are most likely to benefit from chemotherapy or immunotherapy.

It’s precision medicine powered by pathology.

Reflections:

Each of these studies made me think about the balance between trust and technology. We’ve reached a point where AI can truly enhance diagnostic precision—but it also challenges us to stay actively engaged, curious, and informed.

Because the real risk isn’t that AI will outperform us—it’s that we’ll stop thinking critically once it does.

That’s why collaboration between pathologists, data scientists, and industry innovators matters more than ever.

AI isn’t replacing us—it’s redefining what excellence looks like in pathology.

#DigitalPathology #AIinHealthcare #ComputationalPathology #RenalPathology #ThyroidCytology #CancerDiagnostics #DigiPathDigest

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Live from Pathology Visions 2025 in San Diego, I share highlights from Day 2 of the world’s leading digital pathology conference, where experts explored how AI, empathy, and training are shaping the next generation of pathologists.

This episode captures the shift from technology as a tool to technology as a bridge — helping us connect with patients in more meaningful ways.

What I Talk About

1️⃣ From Pixels to Patients
We’ve built the infrastructure; now it’s about applying it. Pathology is no longer just digital — it’s personal, accessible, and human-centered.

2️⃣ Dr. Leah Lijah Joseph’s Keynote — Pathologists as Patients
Dr. Joseph, a cancer pathologist and survivor, shared her journey from diagnosing others to understanding her own slides. She now runs a patient pathology clinic, empowering people to see and learn from their own tissue samples.

3️⃣ The Power of Visualization
Dr. Joseph described how visualization and mental imagery support healing — a reminder that empathy and imagination can coexist with precision science.

4️⃣ AI & Imaging Innovation
From Google Research’s JPEG AXL format reducing file size by 30%, to discussions on color fidelity with DICOM’s David Clooney, we explored how innovation and accuracy must move hand-in-hand.

5️⃣ Cytology Goes Digital
With Hologic’s Genius Digital Diagnostic and AIXMed’s AI-assisted QC, cytology is entering a new era — faster, more accurate, and fully traceable through 100% AI quality control.

6️⃣ The Human Side of AI
I also share a personal story about my mother’s medical experience — and how even with all the tech, empathy remains the missing link. AI can’t replace compassion, but it can help us focus on it by automating what takes time away from patients.

Key Takeaways

• AI is enhancing accuracy and accessibility in diagnostics.
• Pathologists are taking on more patient-facing roles.
• Cytology digitization is revolutionizing quality and speed.
• Innovation must balance efficiency with color and data integrity.
• Empathy and communication will always define great medicine.

I hope this episode helps you see how AI, empathy, and education are shaping the next era of diagnostics.

Let’s continue building the bridge from pixels to patients, one slide at a time. 💡

#PathVision25

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