Implementing Effective AI Governance in Radiology

Penn Medicine governance model centralizes departmental AI review in radiology, aligning clinical priorities with enterprise strategy to enable safer, faster deployments.

The structure defines clear approval pathways through a departmental review that maps to enterprise-level compliance and lifecycle oversight, and it establishes a single escalation route for contract and risk decisions. That operational template clarifies oversight lines and formal approval gates and frames how validation and controls are applied across the department.

Departmental governance formalizes committees and named roles that must approve AI pilots before clinical use. Typical architecture includes an AI oversight committee, departmental clinical leads, an enterprise compliance or lifecycle group, and a single procurement contact who coordinates contract and privacy review. Prioritization follows a rubric that ties clinical need, expected patient impact, and integration feasibility to scheduling and resources, reducing downstream risk by limiting unvetted pilots and clarifying escalation pathways.

Core validation steps before clinical use include technical model validation, retrospective clinical validation on local data, prospective pilot testing, predefined performance thresholds tied to clinical endpoints, and predeclared monitoring plans. Sample size requirements and acceptance thresholds for local holdout testing depend on clinical context and outcome prevalence; institutions should set targets (for example, sensitivity, positive predictive value, or noninferiority margins) with statisticians and clinical experts. An internal validation checklist and test harness enforce reproducible runs, documented datasets, and sign-off criteria—only after these local validation and pilot controls are satisfied does the department move to routine use.

Human factors engineering shapes how alerts and outputs reach clinicians and is essential to safe integration. Activities include clinician shadowing, iterative UI prototyping, alert-fatigue mitigation strategies, clear interpretation and escalation pathways, endpoint-aligned displays, and required training and competency checks. HFE findings frequently change deployments—for example, moving an AI flag from a busy worklist into a gated review flow or embedding aggregate results in a radiology dashboard to preserve workflow continuity—and these adjustments improve day-to-day safety and usability.

Multidisciplinary stakeholder engagement operationalizes governance by bringing radiologists, IT, compliance, quality, informatics, and vendor liaisons together to define requirements and test integrations. Monitoring plans should include real-time performance dashboards, automated drift detection, incident-reporting workflows, and scheduled post-implementation reviews aligned with governance cadence. Continuous monitoring that identifies performance degradation or safety signals feeds back into the governance cycle for reapproval, remediation, or decommissioning and thus sustains safe operations.

Ultimately, operational leaders should adopt a prioritized rubric, enforce local validation checkpoints, and require HFE review as immediate next steps.

Key Takeaways:

• Centralized departmental alignment with enterprise strategy speeds and standardizes approvals — affects departmental leadership and procurement timelines; adopt a single prioritization rubric.
• Mandatory local validation and pilot metrics reduce deployment risk — affects clinical teams and informatics; require predeclared acceptance thresholds before routine use.
• HFE-driven interfaces and monitoring plans improve clinician uptake and safety — affects end users and quality teams; integrate HFE into every deployment checklist.