PACS Integration Strategies for AI Radiology Systems: Best Practices

Successful deployment of AI in radiology depends less on algorithm performance than on seamless integration with existing clinical systems. The most sophisticated AI detection system provides zero clinical value if radiologists never see its results, or if accessing AI outputs requires disrupting established workflows.

This article presents proven integration patterns for connecting AI systems with PACS (Picture Archiving and Communication Systems), RIS (Radiology Information Systems), and EHR (Electronic Health Records), drawn from implementations at academic medical centers and community hospitals.

Understanding the Integration Landscape

Modern radiology departments rely on multiple interconnected systems:

PACS: Archives medical images and routes studies between modalities, viewing workstations, and external facilities. Communicates via DICOM protocol.

RIS: Manages radiology-specific workflows including scheduling, order entry, protocoling, and report distribution. Often speaks HL7 for ADT (admission/discharge/transfer) messages and proprietary APIs for worklist management.

EHR: Comprehensive patient record system that includes demographics, clinical history, lab results, and documentation from all specialties. Integration typically via HL7, FHIR, or vendor-specific APIs.

Viewing Workstations: Diagnostic-quality displays where radiologists interpret studies. May be thick clients from PACS vendor or web-based viewers.

AI systems must integrate with this ecosystem without disrupting existing workflows or introducing new points of failure.

Integration Pattern 1: DICOM Node (Pull-Process-Push)

The most common integration pattern treats the AI system as a DICOM node in the network topology:

Study Routing: PACS forwards completed studies to the AI system via DICOM C-STORE. Routing rules filter by modality, body region, or specific protocols.

AI Processing: System analyzes images, performs detection/measurement/classification, and generates structured findings.

Results Push: AI findings are returned to PACS as DICOM Structured Reports (SR) or as new series (annotated images with overlays). Alternatively, results may be stored in external database and referenced via DICOM key image notes.

Advantages:

• Leverages existing DICOM infrastructure
• Minimal PACS configuration (simple routing rules)
• AI system can process asynchronously without impacting PACS performance

Challenges:

• DICOM SR support varies across PACS vendors
• Some PACS systems treat AI results as separate studies rather than integrating with parent study
• Limited control over when results become visible to radiologists

Integration Pattern 2: Worklist Integration via RIS

For AI systems that influence case prioritization or protocol selection, integration at the RIS worklist level provides optimal workflow:

Query Worklist: AI system queries RIS for active studies matching specific criteria (uninterpreted chest CTs, for example)

Retrieve and Process: Studies are retrieved from PACS for AI analysis

Worklist Annotation: AI results (risk scores, priority flags) are written back to RIS database or transmitted via HL7 messages, updating worklist metadata

Radiologist View: Radiologists see AI-enhanced worklists with high-risk cases automatically promoted to top, color-coded by priority

Advantages:

• Directly influences radiologist workflow without requiring them to seek out AI results
• Enables intelligent triage based on clinical urgency
• Supports protocol optimization before image acquisition

Challenges:

• Requires deep RIS integration, which varies greatly by vendor (EPIC, Cerner, Meditech)
• May need custom database queries or APIs not officially supported
• Risk of workflow disruption if AI system is slow or unavailable

Integration Pattern 3: Overlay Integration via Hanging Protocols

For AI detection systems, the ideal user experience overlays AI findings directly on images within the radiologist's viewer:

Hanging Protocol Modification: PACS hanging protocols are configured to automatically load AI overlay series alongside original images

Registration: AI annotations (bounding boxes, measurements, segmentation masks) are precisely registered to original DICOM coordinates

Interactive Review: Radiologists toggle AI overlays on/off, review findings sequentially, and accept/reject detections

Structured Reporting: Accepted AI findings populate structured reporting templates, minimizing manual data entry

Advantages:

• Maximum integration with radiologist workflow—AI is always present when images are reviewed
• Supports rapid review and confirmation of AI findings
• Enables one-click inclusion of AI findings in reports

Challenges:

• Requires advanced PACS configuration and hanging protocol expertise
• Some PACS vendors don't support overlay registration or provide poor overlay visualization
• Radiologists must learn new interaction paradigms (toggling overlays, reviewing detections)

Integration Pattern 4: Standalone Viewer with PACS Query

Some AI vendors provide dedicated viewing applications that retrieve images from PACS on-demand:

Launch from PACS: Radiologist opens study in PACS, then launches AI viewer via context link or separate application

PACS Query: AI viewer queries PACS using DICOM Q/R (Query/Retrieve) to fetch relevant studies

AI Analysis: Processing occurs in AI viewer with real-time or pre-computed results

Report Generation: AI findings can be copied/pasted or transmitted via HL7 back to RIS for report inclusion

Advantages:

• Minimal PACS integration required—standard DICOM Q/R is sufficient
• AI vendor controls entire user experience and can optimize workflow
• Easy to pilot and deploy without extensive IT involvement

Challenges:

• Context switching—radiologists must leave primary PACS viewer
• Requires separate viewing station or significant screen real estate
• May duplicate image storage if AI system caches locally
• Harder to achieve seamless workflow integration

Integration Pattern 5: EHR-Centric Results Delivery

For health systems with strong EHR platforms (Epic, Cerner), results delivery via EHR may provide best clinician access:

AI Processing: Occurs in background using any of above PACS integration patterns

HL7 ORU Messages: AI findings are formatted as HL7 observation result messages (ORU^R01) and transmitted to EHR

EHR Display: AI results appear in patient chart alongside lab results, prior reports, and other diagnostic data

Clinical Workflow: Ordering physicians and care teams access AI findings directly in EHR without accessing PACS

Advantages:

• Reaches entire care team, not just radiologists
• Supports longitudinal review of AI findings over time
• Enables clinical decision support rules triggered by AI results (e.g., alert for high-risk nodule detection)

Challenges:

• HL7 messaging requires interface engine configuration and testing
• Limited ability to display image context or overlays in EHR
• May overwhelm clinicians with low-level imaging findings typically communicated via radiology reports

Handling Integration Failures and Edge Cases

Robust integration requires graceful handling of failures:

PACS Downtime: AI system should queue studies locally and transmit results when connectivity resumes. Never lose data due to transient network issues.

Duplicate Study Prevention: DICOM routing may inadvertently send same study multiple times. AI system must de-duplicate based on study instance UID to avoid redundant processing and duplicate results.

Partial Study Handling: Studies that arrive incomplete (missing series, corrupted images) should be flagged but not cause processing crashes. Timeout and retry logic prevents indefinite waiting for studies that will never complete.

Version Tracking: When AI algorithms are updated, results should be tagged with algorithm version. This enables performance comparison across versions and auditability of which version produced each result.

Security and Performance Considerations

Encryption: All DICOM transmission should use TLS encryption (DICOM TLS or VPN tunnels) to protect PHI in transit

Authentication: DICOM association negotiation should verify node identity to prevent unauthorized access to images

Performance: Large studies (400+ slice CT angiograms) stress network bandwidth and processing capacity. Compression, prioritization, and asynchronous processing prevent PACS performance degradation

Scalability: Integration architecture must support departmental volume growth—design for 2-5x current volume to avoid near-term capacity exhaustion

Testing and Validation

Comprehensive integration testing should verify:

• Correct study routing to AI system based on modality/protocol filters
• Complete image receipt including all series and multiframe objects
• Accurate AI result formatting (DICOM SR, HL7 ORU, proprietary formats)
• Proper association of results with originating study
• Results visibility in radiologist viewing workflow within defined latency SLA (typically <10 minutes)
• Graceful handling of PACS downtime, network interruptions, and malformed studies

Clinical validation—pilot deployments with small user groups—identifies workflow issues and integration gaps that technical testing misses.

Conclusion

AI integration strategy should be driven by clinical workflow requirements, not technical convenience. The best integration architecture is the one that provides radiologists and clinicians with AI results at exactly the right time, in the right context, with minimal workflow disruption.

Start with clear requirements: When should radiologists see AI results? What actions should they be able to take? What failure modes are unacceptable? Design integration architecture to meet these requirements using proven patterns, test comprehensively, and deploy incrementally with continuous monitoring for performance and reliability.

Done well, integration becomes invisible—AI seamlessly augments clinical workflows as if it were always there. Done poorly, even the most accurate AI system provides zero clinical value and generates frustration, resistance, and eventual abandonment.