In many cases where healthcare providers start utilising AI, the moment an AI model goes live in the radiology department, there is a lack of structured surveillance to monitor and review errors.
Deployment is hard. There are integration battles, vendor negotiations, and radiologist skepticism to manage. When it finally works and the model is running and the worklist is moving, that’s often when things start going unnoticed.
Clinical AI adoption has accelerated rapidly, but deployment practices have not kept pace. Most organizations might face three setbacks the moment a model goes live:
Trust reduction happens. Radiologists encounter an inaccurate finding, get no plausible cause for the inaccuracy, and start ignoring AI outputs altogether - not only because the model is faulty, but because there is no transparency.
Edge cases go unnoticed. The model that performs brilliantly on validation data may struggle with a specific scanner vendor, a rare demographic, or a subtle presentation. Without monitoring, these blind spots stay hidden.
Impact cannot be measured quantitatively. Department heads and hospital leadership cannot answer the most fundamental question: "Is the AI we invested in actually giving a return?"
Think about what actually happens. A radiologist gets a finding flagged that doesn't look right. She checks it, disagrees, and moves on. No feedback loop. No escalation. She just... stops trusting it. Not because someone told her not to but she believes so because of a bunch of wrong cases.
The CARPL AI Monitoring & Alerting Platform is a centralized performance intelligence layer for every AI model deployed in your practice. Think of it as the air-traffic control tower for your AI fleet - always watching and analyzing.
Unlike generic analytics dashboards, the platform is built around three specific clinical realities: performance drifts over time, ground truth is the only honest scoreboard, and radiologists are the most valuable source of feedback in the loop.
Key Features
Comprehensive Performance Dashboards
The platform surfaces near real-time and historical views of every metric that matters - Accuracy, Sensitivity, Specificity, Precision, F1 Score, FPR, and FNR - across all deployed models and clinical sites. Metrics are visualized across daily, weekly, monthly, and yearly time grains so users can detect gradual drift before it becomes a clinical concern. Filters for modality, body part, scanner vendor, patient demographics, and site let teams isolate exactly where performance is strong or fragile.
Ground Truth Integration and Concordance Analysis
Ground truth can be ingested from four sources - the CARPL Widget, CARPL Viewer, direct GT upload, and automated report extraction - and reconciled automatically against AI predictions. The result is a concordance/discordance view at the finding level. False Positives and False Negatives are visualized as color-coded scatter plots, giving radiologists an immediate picture of where the model is over or under-calling findings.
Edge Case Identification
The dashboard lets one identify "far north" cases: highly abnormal studies where the AI produced an unexpectedly low confidence score and "far south" cases: subtle findings missed with high confidence. These are precisely the cases that deplete clinical trust when they go unreviewed. Having them surfaced transforms reactive discovery into proactive quality management.
Dashboard Persistence and Report Export
Every dashboard can be saved with its full filter and layout state, named, and revisited. Dashboards can be shared across key stakeholders and exported as PDFs that include all visualizations and metric insights - ready for department review meetings, governance audits, or executive reporting.
The industry conversation has shifted. Regulators, payers, and accreditation bodies are beginning to ask not just whether a hospital uses AI, but whether it monitors AI. Conferences this past year - from RSNA to HIMSS - reflect a consistent theme: post-deployment performance accountability is the next frontier of responsible AI adoption.
The CARPL AI Monitoring Platform is built to give radiologists the transparency they need to trust the solutions they use. Radiologists get transparency. Department heads get data. And everyone gets a real answer to the question that's always been hardest: is the AI still performing the way it did when we approved it?
If you want to see it in action, talk to our Clinical Solutions Architect and we'll show you how your models are actually performing.
