Scaling Digital Pathology: Overcoming Barriers to AI Adoption Now 

In the evolving intersection of AI and healthcare diagnostics, digital pathology is emerging as a critical frontier. For CIOs, CTOs, pathology directors, and clinical AI teams, the ability to digitize slides and layer intelligent processing can dramatically address both talent constraints and rising diagnostic complexity. This isn’t just about replacing microscopes; it’s about reimagining diagnostic workflows, scaling access, and building sustainable, data-rich imaging pipelines that can support precision medicine at scale.

Yet the transition is far from trivial. It demands significant investments in high-throughput scanning, robust infrastructure, training, and organizational alignment. Understanding how early adopters are navigating these challenges, while building the feedback loops that improve AI models, offers a roadmap. Below is a compelling interview excerpt that nails both the promise and the practical barriers.

As reported in “The growing momentum of AI in digital pathology: An interview with Olga Colgan, PhD” on Medical Laboratory Observer:

“The pathology field, particularly diagnostic pathology, is experiencing a growing imbalance between demand and workforce capacity. On one hand, earlier screening programs and an aging global population are driving a significant increase in the number of samples requiring analysis. On the other hand, advances in personalized medicine, biomarker discovery, and immunotherapy are adding layers of complexity to each diagnosis.

This means not only more samples, but also more tests per sample, and more sophisticated assays overall.

At the same time, the profession is grappling with a global shortage of pathologists. This workforce deficit is contributing to longer turnaround times, delayed diagnoses, and ultimately, delayed treatment for patients.

Artificial intelligence (AI)-driven digital pathology, while still in the early stages, is beginning to gain meaningful momentum as clinical labs look for scalable solutions to address these mounting challenges in diagnostic complexity and workforce capacity. By digitizing slides, labs can:

• Enable remote work.
• Improve flexibility for pathologists. 
• Make the field more attractive to new professionals.

AI tools can then be layered on top of this digital foundation to automate time-intensive tasks, such as slide quality control and slide prescreening, helping labs manage their workload more efficiently and accurately.”

Continue reading “The growing momentum of AI in digital pathology: An interview with Olga Colgan, PhD” on Medical Laboratory Observer. 

Why It Matters to You

For healthcare IT leaders, clinical informatics teams, and decision-makers overseeing lab modernization, this article explores how AI and digital pathology converge to address real constraints, such as workforce shortages and diagnostic complexity. The conversation frames AI not as a futuristic experiment but as an incrementally deployable tool layered upon digitized slide infrastructure.

The insights also signal urgency: winning labs build feedback loops (flywheels) to improve models, push toward standardization, and incentivize digital workflows. For hospitals and diagnostic networks designing an AI strategy, the imperatives are clear: invest in scanning infrastructure, align change management, create hybrid human-machine workflows, and manage regulatory compliance. The winners will be those who convert early gains into sustainable scale.

Next Steps:

• CIO/CTO (Labs/Diagnostics): Perform a digital maturity assessment—how many slides are scanned, infrastructure capacity, network bandwidth. 
• Lab Informatics Lead: Pilot AI prescreening workflows (e.g., QC, artifact detection) on a subset of cases to validate value. 
• Pathology Leadership: Offer training and change management for pathologists to acclimate to digital slide interfaces. 
• Infrastructure/IT Teams: Ensure storage, compute, and image transmission meet latency, throughput, and security demands. 
• R&D/Clinical AI Teams: Create feedback loops by using lab output (ground truth corrections) to continuously refine models and scale adoption.