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.
PRE-DEPLOYMENTEvaluate on your data
Assess model performance on your patient population. Compare vendors. Validate clinical fit before a contract is signed.
IN PRODUCTIONMonitor continuously
Observe operational reliability and clinical alignment in real-world use. Detect drift. Surface the evidence your institution needs.
ONGOING GOVERNANCEAct on evidence
Scale models that perform. Intervene when risk appears. Generate independently publishable evidence. Defend every decision.
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."
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 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.
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.
Is AI running as expected?
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?
Do clinicians trust and act on AI outputs?
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.
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.
Dashboards visualize. Infrastructure governs.
Most institutions are navigating between two inadequate options. deepcOS® introduces a purpose-built neutral infrastructure for institutional AI governance.
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
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
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
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.