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Why are billions of people still invisible in genomic research—and what does that mean for the future of precision medicine?

In this episode, I sit down with Victor Angel Mosti, founder and CEO of Omica.Ai, for one of the most insightful conversations I’ve recorded about data equity and building ethical, community-centered AI.

Victor shares not only his personal cancer story but also the staggering truth: Hispanic and Latino populations make up less than 1% of genomic datasets. This underrepresentation isn’t just a data gap—it’s a clinical risk.

We dive into disparities between healthcare systems, the promise of digital pathology as a low-cost entry point, the dangers of “parachute science,” and how Victor is building a living, ethical, transparent biobank through Omica. AI—built for true precision medicine rooted in community trust.

Highlights with Timestamps

• [00:00–01:40] Personal cancer experiences and diagnostic uncertainty
• [01:40–06:50] Victor’s medical journey across Mexico and the U.S.
• [06:50–11:42] The digitization gap: empathy vs. tech
• [11:42–16:43] The “coffee diversity” metaphor for genomic diversity
• [16:43–19:34] Funding disparities & the biotech cold-start problem
• [19:34–25:44] Digital pathology as a gateway to precision medicine
• [25:44–31:44] Avoiding “parachute science” and building community-first research
• [31:44–36:05] The Nagoya Protocol and benefit-sharing
• [36:05–41:47] Omica.Ai’s work, goals, and clinical-embedded approach
• [41:47–49:36] Creating future-proof, embedded biobanks
• [49:36–53:35] Blockchain for transparency and patient trust
• [53:35–54:39] Victor’s call to action: collaborate, include, and stay human
  
  

Resources from This Episode

• Omica.Ai – Community-driven precision medicine platform
• Nagoya Protocol – Framework for equitable biological use
  
  

Key Insights

• Cancer is personal—even for experts
• <1% representation of Latino genomes threatens clinical accuracy
• Digital pathology + AI can leapfrog infrastructure gaps
• Ethical biobanking requires trust, transparency, and local benefit
• Avoiding “parachute science” is essential
• Genetic diversity drives discovery—but only if we capture it
• Blockchain + dynamic consent = future of patient-centered data
  
  

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How far can AI go in helping us diagnose disease—without losing the human judgment patients rely on?

In this episode, I break down four studies shaping the future of digital pathology, oncology, and neurology. From spatial biology updates at SITC to voice-based Alzheimer’s detection, deep learning for sarcoma prognosis, and new guidelines for safe AI deployment, this week’s digest highlights where AI is making a real impact—and where caution still matters.

Episode Highlights

1️⃣ SITC Trends & Spatial Biology (00:00 → 07:40)

I share key updates from SITC 2025, including the growing role of multiplex immunofluorescence (mIF) and the need for integrated staining-to-scanning workflows. I also preview new educational content and upcoming podcast guests in global AI research.

2️⃣ Digital Neuropathology & Alzheimer’s (07:40 → 13:01)

A major review confirms that digital neuropathology is now robust enough for large-scale Alzheimer’s studies—opening doors for computational tools to link histology with cognition.

3️⃣ Patient Safety in AI (13:01 → 19:56)

An Italian review underscores the foundations of trustworthy AI: dataset quality, transparency, oversight, and continuous validation. I discuss why “patient-centered AI” must remain our standard.

4️⃣ Voice Biomarkers for Cognitive Decline (19:56 → 26:43)

AI models analyzing short speech recordings are showing high accuracy for early Alzheimer’s detection. This could make future screening simple, noninvasive, and more accessible.

5️⃣ Deep Learning for Sarcoma Prognosis (34:06 → 35:59)

A multi-instance CNN outperforms FNCLCC grading by identifying prognostic patterns in tumor center and periphery regions, offering new insights into soft-tissue sarcoma biology.

Takeaways

• mIF is maturing quickly but needs standardized, end-to-end workflows.
• Digital neuropathology is ready for broader Alzheimer’s research.
• Safe AI requires multidisciplinary collaboration and rigorous validation.
• Voice biomarkers may become powerful tools for early cognitive assessment.
• Deep learning can refine prognosis and reveal hidden tumor patterns.

Resources

Hamamatsu (MoxiePlex) • Biocare Medical (ONCORE Pro X) • SITC Programs • Recent publications on AI biomarkers and computational pathology.

Thanks for listening—and for being part of this growing digital pathology community.

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If your pathology reports and other data could talk, what would they say about the future of precision medicine? The truth is, most labs already have the data—they’re just not having a conversation with it.

In this episode, I talk with Peter O’Toole, President and Chief Software Architect at mTuitive. We recorded live at Pathology Visions and are covering the power of structured data and how it’s redefining the future of pathology reporting, AI, and clinical decision support.

We explore how structured reporting evolved from checklists to intelligence, why data hygiene and workflow integration matter more than AI buzzwords, and how collaboration across companies like mTuitive is helping labs turn their reports into clinically actionable data.

Highlights with Timestamps

• [00:00–05:40] Data as the new currency in pathology — Why structured data is the foundation for clinical, research, and trial insights.
• [05:40–10:30] AI & Large Language Models (LLMs) — What AI can (and can’t) do when your data isn’t structured.
• [10:30–19:25] AI workflow integration & voice recognition — How AI and structured reporting work together inside the LIS and IMS.
• [19:25–25:27] Overcoming resistance — Why pathologists initially resisted structured reports and how perceptions are shifting globally.
• [25:27–29:53] Decision support & beyond cancer — Expanding structured data to liver, skin, and even mental health pathology.
• [29:53–34:15] Collaboration as the catalyst — How partnerships create seamless ecosystems for pathology data.
• [34:15–37:03] Demo: Synoptic reporting in action — Real-time staging, automation, and compliance made easy.
  
  

Resources from this Episode

• mTuitive website: https://mtuitive.com
• CAP Synoptic Reporting Protocols – Standardized templates for structured pathology reports.
• Pathology Visions Conference 2025 – Event where this discussion took place.
  
  

Key Takeaways

✅ Structured reporting transforms pathology data from static text into actionable intelligence.
✅ AI and LLMs complement structured data—but can’t replace its clinical readiness.
✅ Clean data in = clean data out—data hygiene defines AI reliability and efficiency.
✅ Workflow integration and user-friendly design drive real-world adoption.
✅ Structured data unlocks clinical trials access, research potential, and decision support tools.
✅ Collaboration is key to building the connected ecosystem pathology needs.

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Is your lab truly digitally ready—or just scanning slides?

That’s the question I unpack in this live discussion from Day 2 of SITC’s 40th Anniversary Meeting, joined by David Anderson (Biocare Medical) and Don Ariyakumar (Hamamatsu Photonics). 

Together, we explore what digital readiness really means for multiplex immunofluorescence (mIF) and how to build reliable, reproducible workflows that scale from research to clinical settings.

What We Discuss

The Discovery Funnel
I open by situating mIF within the broader discovery funnel: researchers begin with hundreds of biomarkers, narrowing down to focused 4–10 marker panels where true clinical utility begins. But this only works if the lab is digitally prepared from the start—from slide prep to data capture.

Defining Digital Readiness
David Anderson reframes digital readiness as everything that happens before the scanner turns on:

• Reagent consistency
• Antibody optimization
• Automation
• Standardized protocols
   All these elements ensure that downstream AI and image analysis tools work on clean, reproducible data instead of “fixing” noise later.

The Pre-Analytical Foundation
Don Ariyakumar emphasizes that scanning can’t fix variability. If staining or section quality isn’t standardized, digitization simply amplifies inconsistencies. True readiness starts at the bench, not the monitor.

Integration Across Vendors
We also talk about how interoperability between stainers, scanners, and spatial biology software is becoming essential. A disconnected workflow—mixing manual, unaligned steps—adds variables that no algorithm can fully normalize.

Lessons from IHC’s Evolution
The team draws parallels between multiplex IF today and IHC’s early days: once complex, now routine. Multiplex IF promises even richer tumor microenvironment insights, but only if standardization and automation catch up to the technology.

Beyond the Funnel
I revisit the “funnel” metaphor in a new light—arguing that as precision medicine grows, the bottom of the funnel broadens, not narrows. That means more tailored, smaller panels rather than one-size-fits-all assays, and a growing need for efficient, reproducible digital workflows.

Key Takeaways

• “Digital readiness” starts before scanning — with chemistry, automation, and process control.
• Consistent pre-analytical quality = reproducible, AI-ready data.
• Interoperability between systems (like Biocare’s ONCORE Pro X and Hamamatsu’s MoxiePlex) accelerates workflow standardization.
• Multiplex IF is maturing quickly, just as IHC once did—on its way to becoming a cornerstone of precision pathology.

Resources Mentioned

🔹 Biocare Medical (Booth 717) — ONCORE Pro X™ open slide stainer automating mIF, IHC, FISH, and ISH protocols.
🌐 biocare.net

🔹 Hamamatsu Photonics (Booth 415) — MoxiePlex™ multispectral imaging platform for high-plex spatial analysis.
🌐 hamamatsu.com

🔹 Society for Immunotherapy of Cancer (SITC) — 40th Anniversary Meeting information and programs.
🌐 sitcancer.org

Timestamp Highlights

00:00 — Welcome from SITC

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Can spatial biology and multiplex immunofluorescence truly transform how we understand cancer?

I went live from the Society for Immunotherapy of Cancer (SITC) 2025 — the 40th Anniversary Meeting to explore how spatial biology, multiplex IF, and digital pathology are coming together to redefine cancer diagnostics, research, and precision medicine.

This session kicked off a weekend of cutting-edge discussions with leaders from Hamamatsu (Booth 415) and Biocare Medical (Booth 717) — two companies helping laboratories around the world embrace digital transformation and spatial imaging in oncology.

🧠 Episode Highlights & Key Moments

0:00 — Introduction
I set the stage live from SITC 2025, explaining the goal of this series: to connect the science of multiplex imaging and spatial analysis with the practical needs of today’s cancer pathologists and researchers.

~1:00 — What Is Multiplex Immunofluorescence (IF)?
I explain how multiplex IF enables simultaneous detection of multiple biomarkers and immune cell types within a single tumor sample — giving us an unprecedented look at the tumor microenvironment and how cells interact.

~2:30 — The Spatial Biology Revolution
We talk about spatial biology as the “next frontier” beyond traditional histopathology — visualizing not just what is on the slide, but where it happens.

~5:00 — Digital Pathology & AI Readiness
I discuss the importance of digital pathology systems for slide digitization and how AI-powered software is now helping identify biomarkers, quantify expression, and accelerate immunotherapy research.

~7:30 — Featured Booths at SITC 2025

• Hamamatsu (Booth 415): High-end slide scanners and digital imaging solutions empowering pathology labs toward digital readiness.
• Biocare Medical (Booth 717): Showcasing the ONCORE Pro X — an open slide stainer that automates multiplex IF, IHC, FISH, and ISH protocols, plus smart software for optimizing complex staining processes.

~9:00 — Real-World Impact
We walk through clinical case examples where multiplex IF data guides immunotherapy decisions — helping clinicians stratify patients and tailor treatments more precisely.

~12:00 — Getting Started
I share practical advice for researchers ready to adopt spatial biology or digital pathology, from workflow design to validation and staff training.

~15:00 — Audience Q&A
Live questions from the audience on implementation, data integration, and scaling multiplex workflows across research and clinical environments.

~20:00 — Future Directions
We look ahead to how machine learning and spatial data integration will shape the next decade of immuno-oncology, including new SITC workshops on AI-driven tissue profiling.

~24:00 — Wrap-Up & Takeaways
Key message: spatial biology is not just a trend — it’s the next layer of precision medicine. I invite everyone to visit Hamamatsu (Booth 415) and Biocare (Booth 717) and to stay tuned for the next livestream focused on multiplex IF in clinical settings.

Resources Mentioned

🔹 Hamamatsu Photonics (Booth 415)
High-performance digital slide scanners and imaging systems.
🌐 hamamatsu.com

🔹 Biocare Medical (Booth 717)
ONCORE Pro X — Open sli

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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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Why do some pathologists still hesitate to trust digital slides—even after the FDA says “yes”? Because accuracy in digital pathology isn’t just about pixels—it’s about precision, validation, and confidence.

In this episode, I talk with Dr. Keith Wharton, MD, PhD, Global Medical Director at Roche Diagnostics, about how the Roche Digital Pathology DX system earned its FDA clearance for primary diagnosis—and what that means for the field.

We explore the science and strategy behind whole slide imaging (WSI) validation, the challenges of feature recognition, the meaning of non-inferiority, and the future of interoperability and AI in diagnostic systems.

If you’ve ever wondered what it takes to make a digital system clinically equivalent to the microscope—this episode is your roadmap.

🔹 Highlights with Timestamps

• [00:00–02:30] What “primary diagnosis” really means—and how the human brain processes histopathology features.
• [02:30–06:00] How Roche achieved FDA clearance for its DP200 and DP600 scanners—and why it’s “clearance,” not “approval.”
• [06:00–11:00] Breaking down the Roche Digital Pathology DX System components: scanner, viewer (Navify DP), and monitor.
• [11:00–16:00] Understanding the pixel pathway—the heart of system validation.
• [16:00–19:00] How the FDA defines precision and accuracy in validation studies.
• [19:00–30:00] Inside the massive multi-year validation studies: design, washout periods, and thousands of slide reads.
• [30:00–33:00] The non-inferiority margin (−4%)—why it matters and how Roche exceeded the benchmark.
• [39:00–45:00] The surprising “nuclear groove” discovery and what it reveals about how pathologists adapt to digital.
• [1:10:00–1:13:00] Future-ready systems and FDA flexibility through predetermined change control plans (PCCP).
• [1:25:00–1:35:00] Keith’s reflection: bridging the gap between discovery and clinical impact, and why the future of digital pathology is brighter than ever.
  
  
  

Resources from This Episode

• Roche Digital Pathology DX (DP200 & DP600) – FDA-cleared systems for primary diagnosis
• FDA Guidance (2016) – Technical performance standards for WSI
• American Journal of Clinical Pathology – Paper in press on validation study design
• Book: Chasing the Invisible by Dr. Thomas Grogan
• Frontiers Journal (2021) – Tissue Multiplex Analyte Detection in Anatomic Pathology (co-authored by Aleks and Keith)
• AJCP Paper
• Aleks and Keith’s Paper - Frontiers | Tissue Multiplex Analyte Detection in Anatomic Pathology – Pathways to Clinical Implementation
  
  
  

Key Takeaways

✅ FDA clearance requires rigorous demonstration of precision, accuracy, and statistical confidence.
✅ Non-inferiority margins (typically −4%) define the threshold for clinical equivalence to microscopy.
✅ Feature recognition in digital environments (like nuclear grooves) challenges perception and training.
✅ Interoperability and predetermined change control plans (PCCP) may accelerate system evolution.
✅ Digital pathology’s foundation is the pixel pathway—where scanner, viewer, and monitor all align.
✅ The field’s future depends on bridging discovery and practice, guided by robust validation.

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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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Live from Pathology Visions 2025 in beautiful San Diego, I sat down with Imogen Fitt from Signify Research to explore how AI, digital pathology, and interoperability are transforming the way we diagnose cancer and deliver patient care.

The conference theme, “From Pixels to Patients,” perfectly captures this year’s shift — from theoretical discussions about AI to real-world implementation and measurable outcomes.

We’re no longer just asking “what can AI do?” — we’re seeing how it’s actually improving accuracy, reducing barriers, and connecting pathologists and labs worldwide.

What We Discuss

1️⃣ From Hype to Application
This year, the buzz wasn’t about AI’s potential — it was about how it’s being used. We highlight case studies showing how digital tools are reducing diagnostic errors, improving collaboration, and even helping smaller labs digitize faster and more affordably.

2️⃣ PathPresenter’s Expanding Role
We dive into PathPresenter’s innovative model that gives users access to digital pathology at no initial cost, opening the door for over 75,000 professionals across 62 institutions. I share why I personally use PathPresenter for teaching and how it’s helping lower the barrier to entry for education, consultations, and patient care.

3️⃣ New Scanning Technology and Accessibility
We talk about compact scanners like Grundium’s four-slide scanner and new miniature models that make digitization possible even in smaller labs. The message is clear: you don’t need a massive system to start going digital.

4️⃣ Collaboration and AI in Action
Imogen shares updates from across Europe and Asia, including how hospitals are tackling storage, AI regulation, and workflow efficiency. We discuss emerging partnerships—Fujifilm, Voicebrook, Dolby, and others—that are making voice dictation, chat agents, and real-time AI insights part of the modern pathology cockpit.

5️⃣ The Human Side of AI Adoption
We also reflect on how digital pathology is changing careers and training. Younger pathologists expect digital tools as part of their workflow — and many won’t settle for less. We discuss how this new generation is driving adoption and pushing institutions to modernize.

My Reflections

I still remember when digital pathology felt intimidating — when only a few people were “allowed” to touch the scanner. But today, that’s changed completely.

Now, we’re living in an era where AI and digital pathology are not optional — they’re essential. The technology has matured, and so has the mindset around it. What excites me most is seeing how collaboration and accessibility are becoming central to innovation.

Key Takeaways

• AI in pathology is moving from hype to practice — focused on improving patient outcomes.
• Accessibility matters: smaller, affordable scanners and open platforms are democratizing digital pathology.
• Collaboration between vendors, clinicians, and technologists is key to faster, smoother adoption.
• The next generation of pathologists expects — and demands — a digital-first workflow.

Listen Now to Learn:

• How AI is reshaping cancer diagnosis
• The tools driving real change in labs today
• How collaboration fuels digital transformation in pathology

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