AI Integration for Imaging Clinical Trials and Research PACS

AI integration for clinical trial imaging focuses on three core surfaces within research PACS like Sectra Clinical Trials, Philips for Research, or Intelerad Research PACS: the image ingestion pipeline, the centralized reading workstation, and the clinical data repository. At ingestion, AI models can perform automated anonymization checks, protocol adherence validation (e.g., slice thickness, sequence parameters), and image quality control (QC) to flag technically inadequate scans before they enter the trial database. This pre-processing layer, often built on event-driven queues listening for new DICOM studies, prevents downstream data integrity issues and manual re-work.

The primary value is in the analysis phase. Integrated AI agents can execute automated quantitative endpoint measurements—such as tumor volume from oncology trials or joint space width from rheumatology studies—directly within the reviewer's workflow. Instead of manual segmentation, the AI processes the series, generates measurements compliant with the trial's RECIST or other criteria, and populates a structured report or DICOM Structured Report (SR). For cohort selection, AI can rapidly screen thousands of archived studies against inclusion/exclusion criteria (e.g., 'presence of a lesion >10mm') and surface a candidate list for the clinical research coordinator, turning a weeks-long manual search into a query that runs in minutes.

Rollout requires a phased, study-specific approach. Start with a single trial arm, integrating AI for a single, well-defined endpoint measurement. Governance is critical: all AI outputs must be traceable (model version, inference timestamp) and configured for human over-read before submission to the Electronic Data Capture (EDC) system. The architecture typically involves a secure, containerized inference service that the research PACS calls via API, with results written back as DICOM SR or to a separate audit database. This ensures the AI operates as a controlled, auditable tool within the regulated clinical trial workflow, not a black-box replacement for human expertise.

Integration begins at the study ingestion layer of the research PACS (e.g., Sectra Clinical Trials, Philips for Research). A DICOM router or listener service monitors for new imaging studies tagged with specific trial identifiers. Upon arrival, studies are routed through a secure, HIPAA-compliant pipeline to containerized AI inference services. For quantitative endpoint analysis—such as tumor volumetry, RECIST measurements, or cardiac ejection fraction—AI models process the DICOM series and return structured results in DICOM Structured Report (SR) or HL7 FHIR Observation format. These AI-generated observations are then attached to the original study in the VNA and pushed to the Clinical Trial Management System (CTMS) like Veeva Vault or Medidata Rave via API, populating eCRF fields automatically and triggering protocol-defined review workflows.

For cohort selection and patient screening, the architecture connects AI to the research PACS's query/retrieve and analytics layer. An orchestration agent uses predefined inclusion/exclusion criteria (e.g., 'identify patients with baseline tumor volume > X cm³') to query the archive. AI models pre-screen historical and incoming studies, generating a candidate list with confidence scores. This list is delivered to the CTMS or a dedicated trial dashboard via a REST API, enabling research coordinators to rapidly identify eligible patients. Automated Quality Control (QC) workflows run in parallel, where AI algorithms check for protocol compliance (e.g., slice thickness, contrast timing, motion artifacts), flagging non-conformant studies for technologist review before they enter the trial's analysis pipeline, preventing data integrity issues downstream.

Rollout requires a phased, study-by-study approach, starting with a single trial arm to validate data flow and accuracy. Governance is critical: all AI inferences must be logged with model version, input hash, and result in an immutable audit trail integrated with the platform's existing audit modules. A human-in-the-loop review interface should be embedded within the research PACS viewer, allowing radiologists or trial adjudicators to easily verify, override, or annotate AI findings before final lock. This architecture, built on secure APIs and containerized services, ensures AI augments the rigorous, audit-heavy workflows of clinical research without disrupting existing SOPs or regulatory compliance.

Integrating AI into research PACS and clinical trial management systems (CTMS) like Sectra Clinical Trials or Philips for Research requires a governance-first architecture. This means designing workflows where AI acts as a controlled assistant within existing audit trails. Key integration points include:

• Study Ingestion Queues: AI models are triggered via DICOMweb STOW-RS or HL7 ORM messages for new imaging submissions, with all inference jobs logged against the study's unique trial and subject IDs.
• Result Storage: AI-generated quantitative measurements (e.g., tumor volume, RECIST criteria) are written back as DICOM Structured Reports (SR) or via FHIR Observation resources, linked to the source images and stored within the research archive for full traceability.
• Approval Gates: Before AI-derived endpoints flow into the CTMS (e.g., Veeva Vault CTMS, Medidata Rave), they pass through a human review step within the research PACS viewer, where the trial radiologist can confirm, adjust, or reject the AI output. This step is mandatory for primary endpoints and is captured as an audit event.

A phased rollout is critical for managing risk and building user trust. A typical implementation follows this sequence:

1. Phase 1 – Automated QC & Anomaly Detection: Deploy AI for silent, behind-the-scenes quality control. Algorithms check for protocol compliance (e.g., slice thickness, contrast timing), detect major artifacts, and flag potential patient ID mismatches. Alerts are routed to a dedicated QC dashboard, not the primary reading workflow.
2. Phase 2 – Cohort Selection Support: Introduce AI for imaging-based cohort selection. Models analyze historical or screening scans to identify subjects meeting specific imaging criteria (e.g., tumor size range, specific biomarker patterns). Results are presented as a filtered list in the research PACS or CTMS interface, with the study coordinator making the final selection.
3. Phase 3 – Endpoint Measurement Assistance: Roll out AI for secondary endpoint measurement (e.g., automated organ volumetry). The AI provides a first-pass measurement and segmentation overlay within the viewer, which the reviewing radiologist must actively accept and sign. All edits are tracked.
4. Phase 4 – Primary Endpoint Workflow Integration: Finally, integrate AI for primary quantitative imaging endpoints, governed by a strict SOP. This involves formal validation against the trial's imaging charter, locked AI model versions, and dual-reader adjudication workflows with the AI as a third, blinded "reader."

Compliance is engineered into the integration layer. The system enforces role-based access control (RBAC) so only authorized users (e.g., principal investigators, imaging core lab staff) can modify or approve AI outputs. All data flows—from ingestion to AI inference to result storage—are logged for 21 CFR Part 11 and GDPR compliance. Furthermore, AI models are containerized and deployed within the health system's or CRO's secure cloud environment (e.g., AWS, Azure), ensuring patient data and trial images never leave the approved boundary. This architecture allows sponsors to maintain control over their intellectual property and trial data while leveraging AI for operational efficiency and precision.