Monitoring and Governance

Govern clinical AI across its full lifecycle

deepcOS® gives academic medical centers the independent, institutional-grade infrastructure to evaluate, monitor, and govern AI before deployment and throughout its clinical life.

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  • PRE-DEPLOYMENT

    Evaluate on your data

    Assess model performance on your patient population. Compare vendors. Validate clinical fit before a contract is signed.

  • IN PRODUCTION

    Monitor continuously

    Observe operational reliability and clinical alignment in real-world use. Detect drift. Surface the evidence your institution needs.

  • ONGOING GOVERNANCE

    Act on evidence

    Scale models that perform. Intervene when risk appears. Generate independently publishable evidence. Defend every decision.

The Governance Gap

Approval is not accountability

Regulatory clearance and local validation are essential starting points. But production is where performance actually matters and where the gaps between controlled testing and clinical reality begin to surface.

Performance changes after go-live

New scanners. Different patient populations. Workflow variation. Updated model versions. What was true at validation may not be true at month six.

Governance relies on vendor claims

Without independent monitoring infrastructure, institutions must trust vendor-provided metrics — the same vendors who benefit from favorable performance numbers.

Accountability without evidence

CMIOs are being asked to stand behind AI decisions they cannot independently verify. When something goes wrong, the institution bears the risk.

"Ensuring that there is oversight… ensuring that the AI products and tools that are being implemented are compliant… looking at bias is extremely important, and having that oversight across the system is extremely important."

 Kristin Myers

Chief Digital Officer & Head of Enterprise Digital Services, Northwell Health

Healthcare regulators, accreditation bodies, and clinical leadership are converging on the same conclusion: post-deployment oversight is no longer optional.

For academic medical centers, the stakes are higher still. Every AI model operating in your environment carries institutional accountability to your patients, your faculty, your IRB, and the broader scientific community.

Governance is not a procurement checkpoint. It is an operational and scientific responsibility.

The Regulatory Horizon

The oversight landscape is shifting fast.

Oversight bodies are moving decisively toward continuous, provider-level accountability, rendering governance approaches that were adequate two years ago increasingly insufficient. AMCs sit at the forefront of this transition due to their complexity and their obligation to produce trustworthy, reproducible evidence.

  • Global regulators’ post-market surveillance framework for AI/ML-based SaMD is expanding in scope and specificity

  • Accreditation bodies are incorporating AI oversight into quality and safety standards

  • IRBs and institutional review processes increasingly require demonstrated performance monitoring for AI used in clinical workflows

  • Journal and registry requirements for AI performance evidence are tightening

What institutions should be able to demonstrate

  • Performance over time

    Evidence that AI models continue to perform as intended beyond initial validation, including sensitivity to population drift, scanner variation, and workflow changes.

  • Subgroup and bias analysis

    Documented monitoring for differential performance across patient subgroups, a requirement that demands event-level data and reproducible methodology.

  • Clinician alignment

    Systematic evidence of how clinical teams interact with AI outputs — not anecdotal feedback, but structured concordance and utilization data.

  • Independent audit trails

    Institutional, vendor-neutral records of AI performance that exist independently of the systems being assessed.

Monitoring in Production

See what AI is actually doing in your environment.

deepcOS® provides standardized, vendor-neutral monitoring across all deployed AI — commercial and in-house — through a single, unified insights environment. Monitoring spans two complementary domains.

OPERATIONAL MONITORING

Is AI running as expected?

Ensure AI runs reliably at scale with clear operational visibility into performance, failures, and system health.

  • Study volumes processed per solution

  • Processing success and failure rates

  • Rejection causes and classification

  • Turnaround times and SLA adherence

  • Solution utilization trends over time

  • Version change detection and tracking

Is our AI infrastructure performing reliably, and do we have the audit trail to prove it?

CLINICAL MONITORING

Do clinicians trust and act on AI outputs?

Transform AI from a black box into observable clinical evidence. Understand how your radiologists actually engage with AI findings.

  • Radiologist concordance rates by model and modality

  • Accept / reject scoring with clinical context

  • Doubt classifications and documented review context

  • Confusion matrices per diagnostic category

  • Version-to-version performance comparisons

  • Version change detection and tracking

Is AI performing in production the way it performed in validation and do we have the evidence to say so definitively?

Actionable AI insights, out of the box

deepcOS® delivers immediate visibility into how AI is operating and performing clinically across your environment. These insights are generated directly from the platform’s monitoring backbone and made available through a unified analytics experience.

Clinical Alignment

Evaluate alignment between AI outputs and radiologist judgment through concordance scoring, confusion matrices, and derived performance metrics such as sensitivity and specificity. This transforms real-world usage into measurable clinical evidence.

Operational Reliability

Monitor study volumes, processing success rates, and solution utilization across your AI portfolio. This visibility ensures AI is running reliably at scale and helps quickly identify processing failures or operational bottlenecks.

Performance Stability

Drill into individual AI solutions to analyze processing times, success rates, and performance trends over time. This allows teams to detect degradation, investigate anomalies, and validate vendor performance in real-world conditions.

Detection Behavior

Observe what AI is detecting across clinical studies, including positive prediction rates and finding distribution. These insights help institutions contextualize model behavior and identify shifts that may require deeper review.

Clinical Alignment

Evaluate alignment between AI outputs and radiologist judgment through concordance scoring, confusion matrices, and derived performance metrics such as sensitivity and specificity. This transforms real-world usage into measurable clinical evidence.

Operational Reliability

Monitor study volumes, processing success rates, and solution utilization across your AI portfolio. This visibility ensures AI is running reliably at scale and helps quickly identify processing failures or operational bottlenecks.

Performance Stability

Drill into individual AI solutions to analyze processing times, success rates, and performance trends over time. This allows teams to detect degradation, investigate anomalies, and validate vendor performance in real-world conditions.

Detection Behavior

Observe what AI is detecting across clinical studies, including positive prediction rates and finding distribution. These insights help institutions contextualize model behavior and identify shifts that may require deeper review.

Governance infrastructure built for institutions where evidence is the standard.

Academic medical centers don't just deploy AI, they study it, publish on it, and are held to a higher evidentiary standard. deepcOS® is built for that reality.

Export-ready evidence for independent analysis

Harmonized, audit-trailed performance data that can be exported to internal BI systems, used to support IRB protocols, and integrated into multicenter AI registries. Your monitoring data is your research asset.

Methodology that holds up to peer review

Standardized metrics and reproducible performance measurement designed to support peer-reviewed publications and institutional reports, based on independently generated institutional data rather than vendor-reported figures.

Govern AI at scale across a complex portfolio

Academic medical centers rarely operate a single AI model. deepcOS® provides harmonized monitoring across vendors, modalities, versions, and clinical domains. One governance foundation for your entire portfolio.

Architecture that doesn't depend on vendor cooperation

deepcOS® sits between your HIT infrastructure and your AI solutions, providing neutral performance monitoring without requiring vendors to supply or validate the data used for governance decisions.

Your data stays yours

No raw imaging data leaves your environment.
Event-level monitoring occurs at the inference layer, preserving institutional data sovereignty while generating the evidence needed for rigorous governance.

Infrastructure for the regulatory questions ahead

Continuous performance monitoring, standardized evidence generation, and audit-trailed records form the institutional infrastructure required to operate confidently as regional post-market surveillance requirements evolve.

Infrastructure vs. Dashboards

Dashboards visualize. Infrastructure governs.

Most institutions are navigating between two inadequate options. deepcOS® introduces a  purpose-built neutral infrastructure for institutional AI governance.

Option A

Vendor Dashboards

  • Metrics defined and provided by the vendor being assessed

  • No cross-vendor comparison or portfolio visibility

  • No access to raw event-level data for independent analysis

  • Governance credibility depends on vendor transparency

  • Data fragmented across multiple proprietary systems

Option B

DIY Monitoring

  • Institutional ownership, but significant engineering burden

  • Custom ETL pipelines required per vendor integration

  • Data normalization across incompatible output schemas

  • Ongoing maintenance competes with clinical priorities

  •  Hard to standardize methodology for cross-institutional comparison

deepcOS®

Neutral AI Infrastructure

  • Harmonized AI outputs across all vendors and versions

  • Event-level audit trails independent of vendor systems

  • Cross-vendor performance comparison on standardized metrics

  • Export-ready data for BI integration, registries, and publications

  • No raw imaging data leaves your environment

Multidisciplinary Governance

Governance is institutional. The evidence should reach everyone who needs it.

Own lifecycle accountability with defensible evidence

Make deployment, scaling, and replacement decisions grounded in institutional data, not vendor claims. When asked to account for AI decisions, have the evidence infrastructure to do so confidently.

Key capability: Portfolio governance across all deployed AI, with audit trails for regulatory reporting and BI integration that elevates AI performance into institutional decision frameworks.

Understand whether clinicians actually trust AI outputs

Move beyond anecdote. Concordance data, accept/reject patterns, and doubt classifications give department leadership a structured, longitudinal view of clinical AI adoption.

Key capability: Clinical monitoring dashboards that reveal alignment between AI findings and radiologist judgment over time.

Generate independently publishable AI performance evidence

Export harmonized, audit-trailed monitoring data to support IRB protocols, multicenter registries, and peer-reviewed publications. Your institution's monitoring data becomes a research asset.

‍Key capability: Export-ready event-level data with standardized methodology designed to support academic publication and regulatory collaboration.

Auditability, sovereignty, and integration

Vendor-neutral infrastructure that integrates with your existing data ecosystem. No raw imaging data leaves the institution. Full audit trails available for compliance and institutional reporting.

Key capability: BI integration, data sovereignty architecture, and harmonized output schemas that reduce maintenance burden.