We’ve operationalized the FDA’s AI monitoring framework end-to-end inside GDAP—the Gesund.ai Development & Assurance Platform. Teams can now continuously track model performance in production, detect statistically significant drift early, and trigger governed responses (notifications, holdouts, re-validation workflows, or PCCP-aligned updates).

GDAP is the first commercially available platform to implement FDA’s AI monitoring approach within a full, inspection-ready AI lifecycle system. This release turns post-market monitoring from a paper concept into a practical, auditable capability for clinical AI.

Raise the Bar on AI Monitoring with FDA Standards

• Regulators are raising the bar. FDA’s program emphasizes methods and tools for effective post-market monitoring of AI-enabled devices—detecting input shifts, tracking output performance, and understanding performance variation causes. Monitoring is no longer optional. It’s core QA.

• PCCP linkage. Continuous monitoring is the operational backbone for safe, pre-authorized model changes under FDA’s Predetermined Change Control Plan (PCCP) framework. If you want to update fast without new submissions, you need robust monitoring evidence.

• Clinical reality changes. Population, scanners, protocols, and referral patterns drift. CUSUM-based surveillance lets you catch small but consequential changes before patient-level harm or KPIs degrade.

What We Built

Our new Monitoring Module implements the FDA’s AI monitoring principles using an approach that is model and modality agnostic—exactly as envisioned by the FDA research team. Key elements include:

• Real-time surveillance of AI outputs with rolling control charts and trendlines to surface subtle shifts quickly.

• Configurable ARL₀ (false-alarm spacing) and ARL₁ (detection delay) targets with normalized thresholds (h, h_0)—so QA owners can tune sensitivity vs. alert fatigue using operating points informed by FDA’s framework.

• Pre/Post change diagnostics: side-by-side scatter/histogram views highlight mean shifts; reference tables show detection delay vs. shift size to make trade-offs explicit.

• Dataset- and model-level registries: tie every alert to the exact model build, data slice, SOP class, scanner/vendor, and site.

• Governed response playbooks: when drift crosses a threshold, GDAP can

  1. alert

  2. quarantine to a holdout queue

  3. open a validation task

  4. launch a pre-specified PCCP workflow

  5. escalate for clinical safety review—with full audit trails

• Multi-tenant, cross-party operation: supports enterprise deployments spanning hospital networks and external partners with strict access controls and immutable logs.

AI monitoring mechanics, selection of monitoring statistics, and operating-characteristic choices are implemented consistent with FDA’s public materials and research discussing the design space of post-deployment monitoring (performativity, metric selection, detection speed).

How it works in GDAP

• Instrument your production inference endpoints (on-prem or cloud).

• Select the monitored statistic (e.g., calibrated score, case-level pass/fail, error proxy) and set ARL₀ / ARL₁ targets.

• Run continuous monitoring with rolling baselines and automated drift alerts via Slack/email/SIEM.

• Investigate with distributions, pre/post means, chart, and reference tables (ARL vs. shift size).

• Respond using governed playbooks—holdout, re-validate, or trigger a PCCP-authorized update path.

Turn FDA’s AIM Monitoring Framework Into a Working System

• We didn’t just add a chart. We implemented the FDA’s statistical monitoring approach and connected it to traceable data lineage, model governance, validation workflows, and PCCP execution inside the same platform.

• The module ships with operational knobs (h, ARL targets) and inspection-ready evidence designed for real clinical environments and audits.

• It is model-agnostic: segmentation, detection, classification, or triage; DICOM or non-DICOM; black-box or white-box. That aligns with FDA’s intent for a general, statistical monitoring tool irrespective of AI type.

Designed for the Realities of Clinical AI

• Small-shift sensitivity. GDAP detects subtle degradations earlier than periodic re-validation windows typically would.

• Explainable operations. Teams can justify thresholds using ARL tables and detection-delay curves, not gut feel.

• Risk-based rollouts. Combine monitoring evidence with PCCP to execute controlled, rapid, and compliant updates.

Availability

The Monitoring Module is available today for GDAP enterprise customers (cloud or on-prem). It integrates with your existing GDAP Data, Models, Annotation, Validation, and Deployment modules and supports VPN-isolated or cross-institutional setups.

See It In Action

• Read FDA’s overview of post-market monitoring methods/tools: U.S. Food and Drug Administration

• Review PCCP implications and why monitoring evidence matters: King & Spalding

• Book a demo to see real-time drift alerts, charts, ARL tuning, and governed response playbooks on your data.