Product

IQC: Integrated Quality Control for AI

Summary

IQC (Integrated Quality Control) is a quality assurance framework that allows expert-oversight directly into medical AI systems. By translating expert data into measurable criteria, it creates a baseline that is then used to continously compare the AI system against.

Problem Statement

Medical AI systems are able to generate outputs at scale, but quality assurance remains a significant bottleneck. Current quality control approaches either make use of additional generative AI systems, or require manual review of every output, creating inefficiencies, inconsistencies, and operator risk. Meanwhile, regulatory frameworks like the EU AI Act and Medical Device Regulation (MDR) demand transparent documentation of AI decision-making and human oversight.

IQC addresses this by automating quality measurement while keeping expert judgment at the center of the process.

System Architecture and Workflow

1. Establishing the required Quality Criteria

The IQC process begins with selecting the relevant quality criteria for your business case or process. Standard measurement dimensions may include:

• Clarity: Output readability and comprehensibility for intended users
• Accuracy: Factual correctness of information presented
• Correctness: Alignment with clinical best practices and guidelines
• Additional custom metrics: Institution-specific quality parameters defined by your team.

Our team will support and guide you through this decision-making process.

2. Establishing the Quality Baseline

Next, IQC will establish an expert-defined quality baseline.

Rather than requiring lengthy configuration or machine learning setup, your domain experts provide either existing clinical guidelines or representative real-world examples of high-quality outputs. These examples are then fed through the IQC Quality Criteria Engine, establishing baseline standard measurements.

The baseline standard can always be updated later, if new data or new quality criteria requirements are established.

3. Quality Measurement

In production, IQC analyzes every AI output against the previously selected quality criteria, generates a quantifiable quality score for each generated output, and comparises the result against your established baseline standard.

4. Real-Time Quality Monitoring and Expert Feedback

Every AI output receives automatic measurement against the baseline standard with live quality indicators. Your team sees exactly where outputs meet expectations and where they fall short. When an output does not meet your quality threshold, the system directly prompts relevant experts for review and correction. This interface requires no technical knowledge; instead experts interact with outputs using familiar clinical workflows.

Continuous Refinement and Learning

Feedback Loop Integration

When an expert revises or corrects an AI output, that correction automatically flows back into the system as implicit feedback. This correction becomes part of the quality baseline standard, continuously refining what "good" means for your context. The system captures not just the final corrected output, but the reasoning and adjustments made by experts.

Adaptive Quality Improvement

IQC employs a rollback-safe learning model: if quality metrics improve after an expert revision, the system advances and incorporates the correction into future quality standards. If quality does not improve or degrades, IQC automatically reverts to the previous version, preventing degradation of your gold standard. This adaptive approach ensures that the quality framework strengthens over time without manual parameter tuning.

Compliance and Audit Documentation

Audit Readiness

IQC automatically generates a comprehensive audit trail without any additional steps from your team.

Every AI output, every expert revision, and every quality decision is documented with full provenance. This eliminates manual audit preparation and creates an immutable record suitable for regulatory inspection, internal compliance reviews, and third-party audits. The system's documentation supports both retrospective compliance validation and ongoing performance monitoring required under emerging AI regulations.

Implementation Considerations

IQC requires minimal upfront configuration. Initial setup focuses on capturing expert examples and defining relevant quality criteria. Once established, the system operates continuously with experts intervening only when quality falls below defined thresholds or when refinement is beneficial. As the expert feedback loop matures, quality standards become increasingly precise and institution-specific, reducing false positives and false negatives in quality assessment.

IQC can be integrated into existing systems using a simple JSON-API, as well as thin Python-SDK.