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What if the biggest breakthrough in pathology AI isn’t a new algorithm—but finally sharing the data we already have?

In this episode, I’m joined by Jeroen van der Laak and Julie Boisclair from the IMI BigPicture consortium, a European public-private initiative building one of the world’s largest digital pathology image repositories. The goal isn’t to create a single AI model—but to enable thousands by making high-quality, legally compliant data accessible at scale.

We unpack what it really takes to build a 3-million-slide repository across 44 partners, why GDPR and data-sharing agreements delayed progress by 18 months, and how sustainability, trust, and collaboration are just as critical as technology. This conversation is about the unglamorous—but essential—work of building infrastructure that will shape pathology AI for decades.

⏱️ Highlights with Timestamps

• [00:00–01:40] Why BigPicture focuses on data—not algorithms
• [01:40–03:16] Scope of the project: 44 partners, 15–18 countries, 3M images
• [03:16–06:20] The 18-month delay caused by legal frameworks and GDPR
• [06:20–11:52] Extracting data from heterogeneous lab infrastructures
• [11:52–13:38] Current status: 115,000 slides uploaded and growing
• [13:38–18:39] Why LLMs and foundation models make curated data more valuable than ever
• [18:39–23:49] Industry collaboration and shared negotiating power
• [23:49–28:06] Data access models and governance after project independence
• [28:06–31:59] Sustainability plans and nonprofit foundation model
• [37:02–43:18] Tools developed: DICOMizer, artifact detection AI, image registration
  
  
  

📚 Resources from This Episode

• IMI BigPicture Consortium
• GDPR & Data Sharing Agreements (DSA)
• DICOMizer & SEND metadata tools
• Artifact detection AI for slide QC
• European AI Factories initiative

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What if the biggest transformation in digital pathology this year had nothing to do with new hardware—and everything to do with how we think about value, workflow, and readiness?

In this year-end recap livestream from the 11th Digital Pathology & AI Congress in London, I break down what truly mattered in 2025. Instead of focusing on buzzwords or hype cycles, this episode highlights the practical advances shaping diagnostics, patient care, and drug development—and the mindset shift our field must embrace to move forward.

Digital pathology is no longer “early adoption.” It’s becoming essential infrastructure. And yet the biggest barrier isn’t scanners or algorithms—it’s the knowledge and confidence needed to use them well.

Key Highlights & Timestamps

0:00 — Setting the Stage from London

An overview of the forces that shaped digital pathology in 2025: workflow integration, clinical readiness, and the move from theory to operational reality.

1:45 — Leica’s Expanded Portfolio & FDA-Cleared Collaborations

A look at Leica’s updated scanner lineup and co-developed, FDA-cleared solutions with Indicollabs. These launches reflect a broader industry trend toward highly specialized, clinically validated digital tools designed for end-to-end workflows.

4:12 — The Acceleration of Companion Diagnostics

From Artera’s de novo–approved prostate prognostic test to AstraZeneca’s TROP2 scoring efforts, 2025 pushed computational pathology directly into therapeutic decision-making.

6:20 — Why Workflow Integration Became the Theme of 2025

Partnerships like BioCare + Hamamatsu + Visgen and Zeiss + MindPeak show where the field is heading: full-stack solutions, not isolated tools. Labs want interoperability, reliability, and simplified digital workflows.

9:10 — Adoption Challenges: ROI, Education & AI Uncertainty

We explore the realities slowing digital transformation:
– ROI is real, but requires workflow change
– AI anxiety persists among clinicians and patients
– Education is still the strongest driver of adoption

12:00 — 2025’s Innovation Highlights

Breakthroughs shaping the next phase of digital pathology include:
– emerging agentic AI platforms
– voice-enabled image management systems
– improved multiplexing technologies like Hamamatsu’s Moxiplex

15:40 — The Growing Intersection of Pathology & Genomics

AI models predicting genomic alterations from H&E images gained traction, especially for cases with minimal tissue. Tempus acquiring Paige signals the deepening connection between digital workflows and molecular data.

18:30 — What 2026 Will Require

Priorities for the coming year include:
– building agentic AI solutions capable of real workflow orchestration
– strengthening validation and QC
– sharing real-world deployment case studies
– expanding training and hands-on learning

RESOURCES:

1. The Lucerne Toolbox 3: digital health and artificial intelligence to optimise the patient journey in early breast cancer-a multidisciplinary consensus

2. Artificial intelligence (AI) molecular analysis tool assists in rapid treatment decision in lung cancer: a case report

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Have you ever thought, “Digital pathology sounds amazing, but without a scanner, what’s the point of learning it now?”
If so, this episode will change how you see your role in the future of pathology.

In this talk, I challenge one of the most persistent myths in our field: the belief that you need expensive hardware before you can begin your digital pathology journey. Through personal experience and the remarkable story of another pathologist who started with even less, I show why knowledge—not infrastructure—is what truly opens doors.

Highlights and Key Themes

0:00 – The Limiting Belief

I open with the core misconception I hear from pathologists worldwide: “I need a scanner before I can start.” I explain why hesitation, not lack of equipment, is the real barrier—and why waiting for perfect conditions keeps many people stuck.

2:24 – My Early Digital Pathology Story

I describe my residency in 2013, when a single scanner was “off limits” to trainees. Faced with a research project requiring consistent cell counting, I improvised using a microscope camera and Microsoft Paint.
It wasn’t sophisticated, but it was digital, consistent, and reproducible.
This experience taught me a foundational lesson: if you can measure something, measure it; don’t rely on visual estimation.

7:01 – How This Led to My First Digital Pathology Job

That basic Paint-and-dots project became my gateway to working at Definiens (now part of AstraZeneca).
I wasn’t hired for computational expertise; I was hired because I understood tissue, biology, and the value of quantifying what we see. Working alongside image analysis scientists showed me the exponential power of combining tissue knowledge with computational tools.

10:03 – Dr. Tala Zafar’s Story

I share the inspiring journey of Dr. Tala Zafar from Karachi, Pakistan, who began with no access to scanners and only a microscope camera.
During COVID shutdowns, she taught herself the foundations of digital pathology, joined global organizations, conducted a nationwide survey, and contacted AI vendors for access to platforms.
After many rejections, one vendor offered a trial account. In just six weeks, she completed three AI projects using microscope camera images—each one published in a peer-reviewed journal.
Her story highlights a universal truth: starting with curiosity and persistence matters far more than having perfect tools.

14:14 – Two Paths After a Conference

I explain the difference between the “forgetting loop” and the “learning path.”
Many attendees leave inspired but slip back into routine. Others commit to one consistent learning habit—journal clubs, vendor webinars, DigiPath Digest sessions—and return a year later with clarity, confidence, and momentum. These individuals become the people others seek out for guidance in digital pathology.

18:04 – Where to Begin

You don’t need a scanner or an institutional budget to start. What you need is structured knowledge.
I introduce my book, Digital Pathology One on One, and encourage listeners to choose one learning habit to build on after the episode. The only wrong choice is choosing nothing.

19:06 – Final Message

Knowledge drives adoption, not infrastructure.
Scanners, AI tools, and computational platforms already exist. What’s missing are people who understand how to interpret tissue digitally, collaborate with computational teams, and bridge biology with technology.
You have

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