Monitoraggio Autonomo della Sicurezza e Sorveglianza della Deriva per l'IA Clinica

TechnoVigilance effettua audit continui sui modelli di deep learning impiegati in radiologia, anatomia patologica e terapia intensiva, garantendo che l'accuratezza diagnostica dell'IA non si degradi nel tempo o tra diverse popolazioni.

TechnoVigilance AI Safety Architecture

Input Distribution Tracking and Explainability Auditing Core

Central System: Clinical AI Vigilance Engine - Drift & Governance Core

Clinical IoMT Nodes

Model Drift Tracker
Input Distribution Monitor
Explainability Audit Log
Diagnostic Discrepancy Flag
Physician Feedback Loop

Architectural Layers & Regulatory Standards

Input Feature Distribution Monitoring (Wasserstein Distance / Population Stability Index (PSI)): Kolmogorov-Smirnov and Wasserstein distance metrics tracking subtle image sensor or patient demographic shifts.
Model Uncertainty & Out-of-Distribution Layer (Conformal Prediction / Deep Ensembles / Epistemic Uncertainty): Conformal prediction and Monte Carlo dropout estimating rigorous algorithmic confidence bounds.
Explainable AI (XAI) Attribution Trails (SHAP (SHapley Additive exPlanations) / Integrated Gradients): Generating SHAP and Integrated Gradients heatmaps stored alongside clinical decisions for legal audit.
Clinical Discrepancy & Retraining Pipeline (Active Learning Triaging / Automated Retraining Triggers): Automated routing of clinician-AI diagnosis conflicts to senior medical review committees.

Key Metrics: < 0.05 (Maximum Acceptable Wasserstein Drift) | 100% (Decision Explainability Audit Coverage) | Zero Hallucination (Confidence-Bounded Clinical Safety) | EU AI Act (High-Risk Medical AI Compliance)

Prevenire il degrado algoritmico silenzioso nella pratica clinica

Clinical AI models trained on one hospital's imaging equipment frequently experience catastrophic accuracy drops when deployed on newer scanners or different patient demographics. TechnoVigilance establishes continuous statistical monitoring to detect covariate shift before patients receive erroneous diagnoses.

Core Engineering Areas

Detecting Covariate Shift in Medical AI — Statistical techniques for detecting when clinical input data diverges from training distributions.
Explainable AI (XAI) Audit Trails — Generating mathematically grounded feature attributions and counterfactual explanations for every diagnosis.
Physician-in-the-Loop Discrepancy Workflows — Resolving disagreements between clinicians and algorithmic outputs to drive active learning.

Technical Articles

Related Cluster Architecture Specifications

HealthVigilance — Zero-Trust IoMT Architecture, SaMD Risk Engineering & Autonomous Clinical Post-Market Vigilance
AgenticSaMD — Autonomous Agentic Software as a Medical Device, Multi-Agent Clinical Decision Support & FDA GMLP Guardrails
AITelemonitoring — Autonomous AI Telemonitoring, Virtual Nursing Assistants & Predictive Clinical Deterioration Scoring
BCI2IoMT — Brain-Computer Interface IoMT Gateways, Neural Decoding & Electrophysiological Streaming