High Throughput JPEG 2000 (HTJ2K): A Practical Path to Faster, Scalable Medical Imaging

Digital Pathology Has Proven Its Clinical Value, Yet Adoption Remains Limited

Digital pathology has reached an inflection point. Regulatory approvals for primary diagnosis continue to expand, AI applications are entering clinical workflows, and healthcare organizations are increasingly recognizing pathology images as strategic enterprise data assets.

Yet digital pathology adoption remains significantly behind radiology and cardiology. A KLAS Research report estimates that fewer than 15% of US healthcare organizations have selected a digital pathology vendor and are at various stages of deployment and adoption. Implementing DP demands significant resources, time, and multidisciplinary expertise beyond simply scanning slides.

Many organizations successfully deploy scanners, viewers, and limited digital workflows but struggle to scale beyond pilot programs or specific use cases. Others maintain hybrid environments where digital workflows coexist alongside traditional glass slide review. The most common reasons for hesitation to adopt digital pathology include the high cost and an unclear return on investment. Other commonly cited barriers include an unstable and evolving regulatory environment, disruption to existing workflows, and lack of organizational buy-in.

The benefits of digital pathology are clear. Slide digitization can revolutionize pathology practice by overcoming the physical limitations of traditional glass-slide workflows and, in turn, addressing some of the profession’s most pressing concerns.

The challenge is scaling digital pathology across multiple laboratories, hospitals, specialties, AI workflows, and enterprise systems while maintaining diagnostic quality, operational efficiency, and long-term interoperability.

Unlike many imaging initiatives, digital pathology requires organizations to simultaneously address workflow redesign, image standardization, infrastructure modernization, interoperability, regulatory compliance, AI orchestration, and organizational change management.

Organizations that treat digital pathology as an enterprise transformation initiative rather than a scanner deployment project are often better positioned for long-term success.

Before deploying digital pathology at scale, healthcare organizations should evaluate the following areas:

Governance and Stakeholder Alignment

Workflow Assessment

Technology and Infrastructure

Integration and Interoperability

Validation and Adoption

Many pathology workflows were designed around physical slides, manual review processes, and localized laboratory operations.Introducing scanners alone rarely creates a meaningful transformation. Successful digital pathology deployments begin with workflow analysis and process redesign.

Organizations should map:

• Specimen accessioning
• Histology workflows
• Slide preparation
• Quality control procedures
• Diagnostic review workflows
• Consultation processes
• Sign-out procedures
• Archiving processes

Many laboratories discover that digitizing inefficient workflows simply transfers bottlenecks from the laboratory bench to the workstation. Laboratory layout often requires modification as well. High-volume scanning operations, digital quality-assurance workflows, and dedicated review workstations may require physical reorganization within pathology departments. Digital pathology adoption succeeds when workflows leverage digital capabilities rather than replicate analog processes electronically.

Diagnostic confidence in digital pathology is how certain a pathologist feels that a diagnosis made from digital slides is accurate and reliable, compared with traditional glass-slide microscopy. Diagnostic confidence in digital pathology depends heavily on image quality because clear, high‑resolution, well‑calibrated images enable pathologists to reliably see fine details and subtle abnormalities, reducing uncertainty in their interpretations.

Organizations should establish technical standards for:

Slide Preparation

• Tissue thickness consistency
• Staining quality
• Coverslip placement
• Barcode placement
• Artifact reduction
• Slide labeling standardization

Scanning Operations

• Resolution standards
• Focus thresholds
• Color fidelity requirements
• Rescan criteria
• Quality control procedures

Without standardized slide preparation and scanning processes, pathologists may encounter inconsistent image quality that slows adoption and reduces confidence in digital workflows.

Automated quality control tools can help identify focus issues, blur, tissue coverage deficiencies, and scanning artifacts before images reach pathologists.

When evaluating digital pathology vendors, many organizations focus primarily on scanner throughput and image quality. While both are important, interoperability often determines long-term scalability.

Organizations should evaluate:

• Scanner throughput
• Image quality
• Enterprise scalability
• Multi-site support
• AI readiness
• Open integration capabilities
• Support for standards-based workflows

The most scalable environments are often scanner-agnostic, vendor-neutral environments capable of supporting multiple vendors and future technology changes.

Technology decisions made during initial deployment can significantly impact future AI adoption, cloud migration initiatives, and enterprise imaging strategies.

Whole slide images are among the largest image objects managed within healthcare environments.

A single WSI image may exceed 1 GB, and complex pathology cases may generate tens or hundreds of gigabytes of image data.

As adoption grows, organizations must support:

• Continuous image ingestion
• High-performance viewing
• Long-term retention
• Multi-site access
• AI processing
• Disaster recovery
• Research repositories

Infrastructure planning should occur before large-scale deployment.

Storage Infrastructure Considerations

Storage requirements often grow faster than anticipated. Organizations should implement scalable architectures that support future growth. SSD storage should primarily function as a high-performance cache and active workflow tier rather than a long-term archive strategy. Long-term retention is typically better supported through object storage architectures that balance performance, scalability, and cost.

Network Infrastructure Considerations

Viewer performance directly impacts pathologist adoption of digital pathology. Fast, intuitive, and high‑quality viewers make digital slide review as efficient and trustworthy as the microscope, increasing pathologists’ willingness to switch to and rely on digital workflows.

Organizations should evaluate:

• Scanner-to-storage bandwidth
• Viewer responsiveness
• Multi-site access requirements
• AI processing workloads

Gigabit network connectivity should generally be considered the minimum baseline for enterprise digital pathology deployments.

Cloud Considerations

Organizations should evaluate:

• On-premises deployments
• Hybrid architectures
• Cloud-native deployments

Cloud infrastructure can provide flexibility for storage growth, disaster recovery, AI processing, and multi-site collaboration, but requires careful planning around performance, security, and governance.

Interoperability is frequently one of the largest barriers to enterprise digital pathology adoption. Most scanners, viewers, LIS/EHR systems, and AI tools use different, often proprietary formats and interfaces, making it technically complex, expensive, and risky to integrate everything into a single, seamless, scalable workflow. The resulting fragmentation increases the risk of vendor lock-in and long-term data obsolescence.

Digital pathology workflows must interact with:

• LIS platforms
• EHR systems
• Enterprise imaging platforms
• AI applications
• Research environments
• Image exchange networks

Organizations should support standards-based interoperability through:

• DICOM
• DICOMweb
• HL7
• FHIR
• REST APIs

Not All Vendors Speak DICOM

Unlike radiology, digital pathology still relies heavily on proprietary image formats. Proprietary slide formats such as Philips iSyntax, Hamamatsu NDPI, Leica Aperio SVS, and TIFF introduce barriers to enterprise imaging integration.

Common formats include:

• SVS
• NDPI
• MRXS
• iSyntax
• BIF
• CZI

These formats can create interoperability challenges as organizations expand digital pathology programs. These limitations increase operational complexity, delay diagnostic workflows, and strain healthcare IT resources. Many healthcare organizations are adopting DICOM normalization strategies to improve long-term interoperability and reduce vendor dependence.

The challenge resembles the transition radiology underwent as it moved from proprietary imaging formats to DICOM-based workflows.

Digital pathology introduces new data governance challenges. Managing the massive file sizes of whole slide images (WSIs) alongside sensitive patient data requires robust policies to ensure interoperability, ethical AI usage, and regulatory compliance.

Organizations must address:

• Data retention
• Security
• Privacy
• Disaster recovery
• Research access
• De-identification workflows

PHI Encoded in Barcodes

Many pathology workflows encode patient identifiers directly within slide labels and barcodes.

Organizations should determine how PHI information will be:

• Extracted
• Normalized
• Mapped to workflow metadata
• Protected during exchange and research workflows

Metadata may require integration through HL7, FHIR, DICOM, or other workflow orchestration mechanisms to ensure consistency across systems.

Artificial intelligence introduces new operational considerations. Artificial intelligence brings new operational demands: the real challenge lies less in deploying algorithms and more in embedding AI seamlessly into everyday clinical workflows.

Organizations must determine:

• Which slides should trigger AI analysis
• Which algorithms should run
• How results should be delivered
• How multiple AI microservices will coexist
• How failures should be monitored

A mature digital pathology environment may support multiple AI applications simultaneously for quality control, cancer detection, biomarker analysis, and research.

AI success depends as much on workflow orchestration, governance, and operational integration as on algorithm performance.

As with any technology, adoption of digital pathology depends heavily on user confidence. There are many technology users in a pathology lab: laboratory technicians who prepare the specimens, make slides, and organize them for review; pathologists who consider all the facts of a case and establish a diagnosis that cannot be contested; and lab administrators who manage people and logistics while delivering outcomes to patients and lab owners.

In promoting digital pathology adoption, organizations should provide comprehensive training programs for:

Pathologists

• Viewer navigation
• Digital workflows
• AI-assisted review

Histotechnologists

• Scanner operations
• Quality assurance procedures
• Exception handling

IT and Enterprise Imaging Teams

• Infrastructure management
• Monitoring
• Troubleshooting
• Integration support

Change management programs should accompany technical deployments to ensure successful adoption.

Clinical deployment requires validation.

Organizations should complete:

• Validation studies
• Workflow testing
• User acceptance testing
• Regulatory reviews

Many organizations begin with a limited-scope proof of concept that compares:

• Analog diagnosis
• Digital diagnosis

Key metrics include:

• Diagnostic concordance
• Turnaround time
• User satisfaction
• Workflow efficiency

These evaluations help identify operational issues before broader deployment.

Digital pathology should be treated as an evolving operational program.

Organizations should continuously monitor:

• Diagnostic turnaround times
• Scanner utilization
• Storage growth
• AI utilization
• Workflow bottlenecks
• User adoption
• Quality metrics

Regular review helps identify opportunities for optimization and future expansion.

1. Workflow and Process Redesign
   Map and optimize workflows before implementation.
2. Slide and Image Quality Standards
   Establish technical standards for slide preparation, scanning, and image validation.
   
3. Integration and Interoperability
   Enable seamless connectivity across LIS, EHR, AI, and enterprise imaging systems.
4. Data Management and Regulatory Compliance
   Implement secure storage, privacy controls, governance policies, and retention strategies.
5. Training and Adoption
   Support organizational change through structured training and user engagement.
6. AI Orchestration and Workflow Automation
   Design scalable workflows that support current and future AI initiatives.

1. Engage pathology, laboratory, enterprise imaging, IT, compliance, and research stakeholders early.
2. Map and optimize existing workflows before digitization.
3. Establish standardized requirements for slide preparation and image quality.
4. Expand the storage infrastructure and use SSDs primarily as a high-performance cache tier.
5. Upgrade network infrastructure to support gigabit or faster connectivity.
6. Segregate digital pathology infrastructure, both logically and operationally, from conventional imaging environments.
7. Design adaptable workflows capable of supporting multiple WSI vendors.
8. Develop a strategy for converting proprietary formats to DICOM.
9. Plan for metadata normalization and barcode-derived PHI management using HL7, FHIR, and standards-based workflows.
10. Support multiple transport methods including DICOM, DICOMweb, REST APIs, Amazon S3, FTP, and SFTP.

Digital pathology adoption requires more than scanners and viewers. Healthcare organizations need an interoperability and workflow orchestration layer that connects scanners, LIS platforms, EHR systems, enterprise archives, AI applications, cloud infrastructure, and clinical workflows.

The Unifier^® platform helps healthcare organizations:

• Route and orchestrate whole slide imaging workflows
• Normalize proprietary pathology formats
• Support DICOM Whole Slide Imaging initiatives
• Integrate LIS, EHR, HL7, FHIR, and enterprise imaging systems
• Automate AI workflow orchestration and result delivery
• Enable secure image exchange and collaboration
• Support cloud, hybrid, and on-premises deployments
• Scale digital pathology environments across multiple hospitals and laboratories

The Unifier platform is purpose-built to overcome the challenges posed by proprietary digital pathology formats. Deploying the Unifier platform eliminates ingestion bottlenecks, improves diagnostic turnaround times, and prepares healthcare organizations for AI-driven pathology initiatives. Embracing vendor-neutral DICOM solutions is not just a technical necessity; it’s a deliberate move toward a more efficient, accessible, and AI-driven future in pathology.

Whether supporting an initial deployment or expanding an enterprise-wide digital pathology strategy, Unifier provides the interoperability foundation needed to simplify integration, automate workflows, and support long-term growth.