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For decades, pathology has been one of the most analog corners of modern medicine. While specialties like radiology embraced digital imaging years ago, pathologists continued to rely on glass slides, microscopes and physical storage systems. But this is starting to change.
Driven by advances in enterprise imaging, artificial intelligence (AI) and precision medicine, digital pathology is becoming a critical tool. It enables faster diagnoses, greater collaboration and more personalized treatment, especially within large, complex systems like the US Department of Veterans Affairs (VA).
Despite the rapid adoption of AI across healthcare, pathology has been slower to digitize. The global digital pathology market is growing at nearly 10 percent annually¹, yet most pathologists in the US still read physical slides.
“From an AI perspective, the majority of pathologists are still working off of glass slides,” explains Dr. Heather Chait from Philips. “They’re not digitized to even apply AI. You’ve got to have them in a digital realm before you can even move to that next phase.”
Several factors have contributed to this lag. Early on, there was concern that AI might replace physicians, much like the initial fears in radiology. Cost and change management have also played a role. Many practicing pathologists were trained on microscopes and face a learning curve when transitioning to digital platforms.
However, the pressures on pathology departments are mounting. Cancer diagnoses and biopsies are increasing, while a large portion of the pathology workforce is nearing retirement. With fewer new specialists entering the field, workloads are growing. Digital transformation is no longer just an option; it is essential for sustaining quality care.
At its core, digital pathology replaces fragile glass slides with high-resolution digital images. Whole-slide scanners capture these images, which are then stored as secure files that can be viewed, shared and analyzed across networks. This shift solves practical problems that have long troubled pathology labs.
“With glass slides, when you pulled out a box from five years ago, the hematoxylin and eosin (H&E) stain has faded,” Chait says. “The images are not vibrant. Through digital scanning, those vibrant representations live on. They don’t fade, they don’t break. You really are preserving that longitudinal comparison for the future for each patient.”
Beyond preservation, digitization enables computational analysis. AI tools, many of which are FDA-cleared, can assist with tasks like Gleason scoring in prostate cancer, reducing subjectivity and variability among readers.
“It reduces reader variability,” Chait notes. “So you have reproducible, consistent results.”
Instead of replacing pathologists, AI acts as an efficiency and standardization tool. It helps surface insights, highlight suspicious regions and ensure consistency across large health systems.
Modern digital pathology systems are integrated ecosystems. Scanners convert slides to a digital format, an image management system stores the files, and clinical viewers allow pathologists to analyze cases and collaborate in real time.
Crucially, enterprise-grade platforms support interoperability. Digital slides are stored in DICOM format – the same standard used in radiology. This allows for integration with electronic medical records (EMRs), PACS systems, and even genomic data. This interoperability transforms pathology from an isolated function into a key part of precision medicine.
“We can now pull together the EMR data, the imaging, the genomics and digital pathology,” Chait says. “It allows fully integrated diagnostics and drives precision care.”
This means clinicians can increasingly move away from a one-size-fits-all model and tailor therapy to a patient’s specific tumor characteristics.
Few healthcare organizations operate at the scale of the VA, which processes approximately 950,000 pathology cases each year². By policy, every cancer case must be read twice before confirmation, which effectively doubles the workload. Before digitization, getting a second opinion often meant mailing slides, a process that could take five to seven days.
Today, digital pathology is deployed in about one-third of VA medical centers, with a national program guiding a phased expansion. Sites are prioritized based on staffing shortages and clinical need. The impact has been significant. AI-assisted workflows have shown efficiency gains of around 27 percent or more in some settings³. More importantly, digital sharing enables near real-time consultations and second opinions.
“When you hear that there’s something abnormal, each minute feels like a day,” Chait says. “We now have the ability to not only look at the tissue sample but also address the mental health concern associated with waiting time for the patient.”
The benefits are especially great for rural veterans. About 40 percent of the veteran population lives in rural communities. Digital pathology allows slides prepared in one location to be reviewed by specialists elsewhere without delay, expanding access to expert care regardless of geography.
Christina Nichols, Executive Leader, US Department of Veterans Affairs at Philips, adds, “by adopting digital pathology and capabilities that can streamline diagnosis, we're able to help those rural communities as well.”
Transitioning to digital pathology requires more than just scanners. Health systems must invest in high-performance storage, secure networking, workflow redesign and clinician training. Change management is central to this process. Pathologists must adapt from microscopes to large monitors, and IT teams must plan for massive data volumes.
Comprehensive support, including workflow consultation and implementation planning, helps smooth the transition and ensure sustained adoption.
Looking ahead, the convergence of digital pathology, AI, and pharmaceutical research could further reshape cancer care. Pharma companies are investing heavily in biomarker discovery, using AI to extract deeper insights from pathology images.
The future may extend even further. Emerging concepts like “digital twins” – virtual simulations of individual patients – could allow clinicians to test treatment strategies computationally before applying them.
“We may be able to use pathology slides in a more diverse manner and extract more information from them,” Chait says. “If we can identify more biomarkers, we can give patients more treatment options.”
Five-year risk predictions derived from pathology data are also on the horizon. This could allow providers to proactively enroll patients in surveillance programs based on individualized risk modeling.
By converting fragile glass slides into secure, analyzable data, enterprise digital pathology is helping large systems like the VA accelerate diagnoses, reduce clinician burden and expand access to expert care. The technology is digital, but the outcome is profoundly human: faster answers, clearer insight and more personalized care.
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