How does the AI assistant integrate with PACS and our existing RIS/EHR without disrupting workflow?
The assistant connects to imaging and clinical systems through standard interfaces: DICOM ingestion for series and metadata, PACS/VNA queries for priors, and HL7v2 or FHIR for encounter context. Integration is implemented as an adjunct service that writes draft reports into the same report queue or a review workspace so radiologists continue to use their existing viewer and sign-out process.
What controls exist so radiologists retain final sign-off and liability for reports?
By default, AI produces clinician-editable drafts that require explicit radiologist sign-off before a report is finalized. Versioned drafts, edit tracking, and visible audit notes record which sections were AI-generated and who reviewed or changed them, preserving clinical ownership and a clear chain of responsibility.
How are PHI and patient images protected during inference and when exporting AI-assisted drafts?
Options include on-premise inference, private-cloud deployment within a hospital VPC, encrypted transports for DICOM/HL7/FHIR, and configurable redaction templates for exports. Audit logs and role-based access controls limit who can view or extract PHI-containing artifacts.
What validation steps are recommended before deploying assistance in clinical reads?
Start with a scoped pilot: select modalities and read types, run AI drafts in parallel with normal reads, review disagreements in multidisciplinary meetings, document acceptance criteria (edit rates, false-negative tolerance), and define a monitoring plan for ongoing performance and drift detection.
Can the assistant compare current studies to priors automatically, and how are priors matched reliably?
Yes. Priors are matched using DICOM identifiers, accession numbers, study dates, and patient demographics. Matching logic can be tuned to prefer same-protocol priors or the most recent studies within a configurable timeframe; mismatches are flagged for radiologist review.
How does the platform surface explainability and evidence for AI-suggested findings?
Explainability views link suggested findings to the supporting DICOM series, key frames, and relevant metadata fields. Audit notes include the prompt used and confidence indicators. These artifacts help radiologists verify suggestions quickly and incorporate or reject them with clear traceability.
What deployment options support hospitals with strict data-residency or on-prem requirements?
Deployments can be fully on-premise behind hospital firewalls or provisioned in a private-cloud arrangement that keeps PHI within a customer-controlled VPC. Network and export policies are configurable to meet institutional requirements.
How are audit logs, versioning, and change history handled for regulatory and QA reviews?
All AI drafts, edits, reviewer notes, and final sign-offs are versioned and stored in an immutable audit log. Logs are queryable for QA reviews and can be exported in formats commonly used for compliance audits.
What processes support model updates, and how are update impacts evaluated clinically?
Model updates follow a staged workflow: dev validation, internal QA, a controlled pilot, and monitored rollout. Clinical impact is evaluated using parallel-read metrics, clinician feedback, and post-deployment monitoring to detect changes in edit patterns or disagreement rates.
How can radiology teams customize language templates, urgency thresholds, and follow-up suggestion logic?
Teams can adapt built-in templates and prompts to institutional style guides and set configurable thresholds for urgency flags and follow-up logic. Template changes and threshold settings are logged, and preview testing with historical studies is recommended before applying them to live reads.
