Native DICOM Output Redraws the Pathology Map

The long-awaited arrival of native DICOM output in digital pathology signals more than a technical milestone. It marks a strategic convergence of imaging standards, data architecture, and AI readiness across healthcare enterprises. As vendors begin aligning digital pathology workflows with established medical imaging protocols, health systems face an opportunity, and an obligation, to reassess how pathology data is stored, shared, and analyzed at scale.

The announcement by Philips of its SGi digital pathology scanner with native DICOM JPEG and JPEG XL output is not merely a first-to-market achievement. It represents a deliberate shift away from proprietary silos and toward interoperable, cross-modality infrastructure. For pathology leaders already navigating rising diagnostic workloads and workforce shortages, the implications are substantial: streamlined workflows, reduced storage overhead, and expanded integration with enterprise-wide diagnostic tools.

From Proprietary Islands to Interoperable Terrain

Unlike radiology and cardiology, pathology has historically lagged in adopting imaging standards like DICOM (Digital Imaging and Communications in Medicine). This gap left labs dependent on vendor-specific formats, hindering interoperability and complicating AI deployment. Proprietary image types are not only difficult to scale across systems, they also limit the ability to integrate pathology data with enterprise imaging platforms or advanced analytics environments.

Now that Philips is embedding configurable DICOM JPEG and JPEG XL output natively into its pathology scanners, it sets a precedent for a vendor-neutral imaging future. JPEG XL, in particular, offers up to 50% smaller file sizes without compromising diagnostic quality, an essential advantage as digital pathology labs generate terabytes of high-resolution slide data weekly. With native support for a standardized output, labs can store these images in a vendor-neutral archive (VNA) or a PACS environment alongside other clinical imaging, improving both access and analytic potential.

The benefit is not theoretical. As Signify Research analysts note, the adoption of standardized formats in digital pathology reduces infrastructure costs and enables tighter integration with AI tools. In practice, this means AI-driven diagnostics, clinical decision support, and telepathology platforms can be deployed more rapidly and securely, without needing translation layers or conversion software.

Digital Pathology as a Systemic Investment

The implications extend well beyond individual labs. With an estimated 70% of clinical decisions relying on laboratory data, and pathology playing a central role in cancer diagnosis, the efficiency and accuracy of digital pathology systems have direct consequences for patient care. Yet most health systems still treat pathology modernization as a local departmental initiative rather than a strategic infrastructure investment.

The emergence of native DICOM in digital pathology provides CIOs and CTOs with a strong architectural rationale to integrate pathology into enterprise imaging strategies. By aligning pathology imaging formats with those already used in radiology and cardiology, IT leaders can eliminate parallel infrastructure and begin consolidating storage, viewer platforms, and analytic environments across disciplines. In hybrid or cloud-based models, the bandwidth and cost efficiencies introduced by JPEG XL compression become even more compelling.

This shift also supports broader system goals, including cross-site collaboration, remote diagnostics, and unified patient imaging records. As CMS and other regulatory bodies continue to push interoperability mandates, DICOM-aligned pathology platforms simplify compliance and enable more effective participation in health information exchanges.

AI at the Edge of Practical Deployment

For AI to become more than a research tool in pathology, it needs access to standardized, high-quality datasets at scale. Non-standard formats have long impeded this by fragmenting image access and slowing down model training and validation. Now, with scanners outputting directly in DICOM JPEG XL, AI developers can work with consistent, metadata-rich files that integrate seamlessly into clinical pipelines.

This is a critical step for labs under pressure from workforce shortages. According to the Association of American Medical Colleges, the U.S. faces a growing shortage of pathologists, just as cancer caseloads continue to rise. AI-powered tools can augment diagnostic capacity, but only if they are trained and deployed on interoperable platforms. Native DICOM output streamlines this, offering a ready foundation for computer-assisted diagnosis, quality assurance automation, and even predictive modeling.

Cloud partnerships, like Philips’ collaboration with Amazon Web Services, add further scalability by enabling remote image storage and analysis. But these benefits can only be realized when image formats are standardized and widely accepted across vendor ecosystems. DICOM, with its long-standing governance and wide adoption in other imaging fields, provides a viable path forward.

Enterprise Alignment Is No Longer Optional

Healthcare leaders responsible for imaging strategy, digital transformation, or diagnostic service lines should treat the emergence of native DICOM in pathology not as a feature update but as a systemwide trigger. The move away from proprietary imaging toward open, compressed, standardized formats is a prerequisite for AI, a cost-saver for IT, and a productivity gain for clinical teams. It also paves the way for centralized imaging strategies that unify diagnostic data across service lines.

The question is no longer whether digital pathology will become interoperable. It already has. The real decision now lies with executive teams: whether to treat pathology as a siloed specialty, or as a foundational component of enterprise imaging that requires integration, investment, and long-term strategic planning.

Failure to act may not lead to immediate disruption, but it will ensure fragmentation and inefficiency persist at precisely the moment health systems are seeking data harmonization. The capabilities are here. The standards are maturing. It’s time for leadership alignment to catch up.