Building Effective Concordance Registries: Tracking AI-Human Agreement in Radiology

As AI systems become integral to radiology workflows, a critical question emerges: how do we measure and monitor their real-world performance over time? Academic validation studies provide important baseline metrics, but clinical deployment introduces new variables—different patient populations, scanner variations, evolving practice patterns—that can impact AI performance in unpredictable ways.

Concordance registries—structured databases that systematically track agreement between AI recommendations and physician decisions—provide the answer. By logging every AI-human interaction and measuring agreement rates across multiple decision points, these registries enable continuous quality improvement, identify algorithm failure modes, and support regulatory compliance.

What Gets Measured: Key Concordance Points

Effective concordance registries capture agreement at multiple touchpoints in the radiology workflow:

Pre-triage vs. Final Risk Assessment: When autonomous AI systems assign risk groups to lung nodules (1-5 scale based on malignancy probability), radiologists confirm or adjust these assignments post-interpretation. High concordance (>90%) indicates accurate AI risk stratification; systematic disagreements in specific scenarios identify opportunities for algorithm refinement.

Protocol Suggestions vs. Selected Protocols: AI-recommended examination protocols are compared against protocols actually used by technologists and radiologists. Acceptance rates above 85% suggest appropriate matching of clinical indication to protocol; lower rates may indicate gaps in the protocol library or inappropriate suggestion logic.

Guideline-Based Tactics vs. Physician Choices: For management decisions (follow-up intervals, biopsy recommendations), concordance registries track agreement between evidence-based guideline recommendations and actual physician selections. Patterns of systematic deviation may reflect evolving clinical standards, unique institutional practices, or gaps in guideline applicability to edge cases.

AI Detections vs. Radiologist Reports: Comprehensive tracking of which AI-detected findings make it into final radiologist reports, which are acknowledged but dismissed as false positives, and which findings are missed by AI but reported by radiologists. This granular tracking enables precise measurement of sensitivity, specificity, and false positive rates in real-world practice.

Registry Architecture and Data Capture

Successful concordance registries require automated data capture from multiple systems:

• RIS/EHR integration for exam metadata and clinical context
• PACS integration for AI-generated structured reports and radiologist findings
• Dedicated decision capture interfaces for real-time agreement/disagreement logging
• Temporal tracking to analyze trends and detect performance drift

The registry must preserve sufficient context to enable meaningful analysis—not just "AI said X, radiologist said Y" but why disagreements occurred, what patient factors influenced decisions, and how recommendations evolved over time.

Critically, registry design must balance granular data collection with minimal workflow disruption. Radiologists should be able to confirm agreement with a single click while providing brief justification for disagreements—friction in data capture leads to incomplete registries that limit analytical power.

Analysis and Actionable Insights

Raw concordance rates tell only part of the story. Deep analysis reveals:

Systematic vs. Random Disagreements: Random disagreements clustered around decision boundaries (e.g., nodules near size cutoffs) differ fundamentally from systematic disagreements where AI consistently under- or over-calls risk in specific clinical scenarios. Systematic patterns drive algorithm updates; random variation near boundaries is expected and acceptable.

Reader-Specific Patterns: Tracking concordance by individual radiologist identifies outliers—both those who rarely agree with AI (suggesting skepticism or local practice variation) and those who always agree (suggesting over-reliance on AI recommendations). Targeted education addresses both extremes.

Temporal Trends: Concordance rates that drift over time may indicate algorithm performance degradation, changes in patient population, or evolving clinical standards. Early detection enables proactive intervention before quality impacts accumulate.

Predictive Features: Identifying patient and image characteristics associated with disagreement (e.g., "AI and radiologists frequently disagree on part-solid nodules <8mm") enables both algorithm improvement and educational interventions.

Regulatory and Accreditation Value

Concordance registries provide essential evidence for regulatory post-market surveillance and institutional quality programs:

FDA Post-Market Requirements: For cleared AI devices, tracking real-world concordance demonstrates continued safe and effective use, identifies previously unknown failure modes, and supports regulatory reporting obligations.

ACR Accreditation: Comprehensive quality registries satisfy ACR requirements for peer review, quality improvement activities, and systematic performance monitoring.

Malpractice Defense: Detailed audit trails showing appropriate use of AI decision support, documentation of disagreements, and physician oversight provide strong evidence of standard-of-care practices.

Closing the Loop: Continuous Improvement

The ultimate value of concordance registries lies in their ability to drive systematic improvement. Quarterly reviews by radiology leadership and AI vendors identify:

• Edge cases requiring algorithm retraining
• Protocol gaps necessitating catalog expansion
• Guideline updates requiring system reconfiguration
• Educational opportunities for radiologists and technologists

This closed-loop process—measure concordance, analyze patterns, implement improvements, re-measure—ensures that AI systems evolve with clinical practice rather than becoming static tools that gradually drift out of alignment with current standards.

Building Registry Infrastructure

Institutions implementing AI in radiology should prioritize concordance registry infrastructure from day one. Key success factors include:

• Executive sponsorship to ensure cross-departmental data access
• Dedicated data analyst resources for ongoing registry maintenance
• Regular review meetings with defined stakeholders and decision authority
• Transparent communication of findings to frontline radiologists
• Formal process for translating insights into action

The investment in registry infrastructure pays dividends through improved AI performance, enhanced physician confidence, regulatory compliance, and most importantly, better patient care through systematic quality improvement.