Automated BI-RADS, LI-RADS, and PI-RADS Scoring Workflow

This custom workflow automates the application of BI-RADS, LI-RADS, and PI-RADS scoring frameworks to segmented imaging features, directly addressing the operational bottleneck of manual, subjective scoring in high-volume screening. The business case is clear: a 30-50% reduction in radiologist scoring time per case, elimination of administrative coding errors, and standardized reporting that improves auditability and supports population health analytics. Implementation requires orchestrating rule-based and ML-driven scoring logic that ingests DICOM metadata and segmentation masks from a PACS or AI inference pipeline, then applies framework-specific criteria to generate a structured score and draft report impression.

Architecturally, the workflow is built on an orchestrator like LangGraph, which manages specialized agents for feature extraction, framework-specific rule evaluation, and confidence scoring. Critical controls include an audit trail logging every scoring decision and physician override for compliance, a confidence threshold gate that routes low-certainty cases to a human review worklist in the RIS, and real-time performance monitoring against historical gold standards. Integration points are the PACS for image retrieval, the RIS for worklist management, and the EHR via HL7/FHIR for final report insertion, ensuring the automated scores become part of the legal medical record without manual transcription.

This workflow automates the conversion of raw segmentation masks and quantitative radiomic features into structured, framework-specific scores. It eliminates the manual, error-prone steps where radiologists or technologists must visually assess lesions, measure dimensions, and cross-reference complex scoring tables. The operational upside comes from a 40-60% reduction in per-case scoring time, improved coding consistency for billing and analytics, and the ability to handle screening volume surges without adding specialized staff. The system ingests DICOM Structured Reports from segmentation pipelines and patient context from the EHR via FHIR.

Implementation integrates a rules engine (e.g., Drools) for deterministic criteria and an ML classifier for nuanced feature synthesis, both hosted within a LangGraph orchestration. Each scoring agent outputs a final category, a confidence score, and cited evidence. Low-confidence scores are routed to a human-in-the-loop review queue within the existing radiologist worklist (e.g., integrated with Nuance PowerScribe). The architecture includes mandatory audit logging of all scoring decisions and physician overrides to satisfy compliance (MIPS, ACR) and support continuous model retraining. Deployment is typically containerized (Kubernetes) with APIs to major PACS (e.g., Epic Radiant, GE Centricity) and RIS systems.

The operational upside comes from eliminating manual scoring lookup and coding, which consumes 3-5 minutes per screening study. A custom workflow ingests segmentation masks and extracted radiomic features from PACS, applies rule-based and ML-driven scoring logic from validated frameworks, and drafts structured reports with the assigned category and evidence. This directly reduces radiologist cognitive load and administrative labor, improving report turnaround and billing accuracy while creating a consistent, auditable scoring trail essential for accreditation and quality benchmarking.

Implementation follows a phased safety-first approach. Phase 1 deploys the orchestrator (LangGraph) for non-critical mammography BI-RADS scoring in a shadow mode, logging all outputs against human reads. Phase 2 integrates the workflow with the RIS (e.g., Epic Radiant) for draft report generation, requiring physician sign-off. Phase 3 adds LI-RADS/PI-RADS logic and enables autonomous scoring for high-confidence cases, with low-confidence results and all overrides routed to a dedicated review queue in the PACS worklist. Each phase includes validation against a gold-standard dataset and continuous monitoring via a dedicated dashboard tracking scoring drift and override rates.

The governance model is built on a multi-tiered control architecture. A central orchestrator, typically built with LangGraph or a similar framework, manages the flow from PACS ingestion through segmentation, feature extraction, and rule-based scoring. Every decision point—such as applying a BI-RADS 4 versus 3 score based on segmented lesion morphology—is logged with the underlying image features and model confidence scores. This creates an immutable audit trail, essential for compliance with FDA 510(k) or CE marking for software as a medical device (SaMD) and for defending against potential liability. The system must integrate with the hospital's identity and access management (IAM) to enforce role-based access, ensuring only credentialed radiologists can approve or override final scores before they are committed to the RIS and EHR.

Rollout must be phased, starting with a non-diagnostic shadow mode. The automated scores run in parallel with standard workflow for a pilot group, allowing validation of accuracy and inter-reader agreement without impacting patient care. Following validation, a pilot launch in a single screening program (e.g., mammography) introduces the system as a decision-support tool, requiring radiologist confirmation for all scores. The final phase expands to full autonomy for high-confidence cases, automatically populating structured reports. Continuous monitoring via dashboards tracks key metrics: scoring throughput, override rates, and model performance against a gold-standard review panel. This controlled, metrics-driven approach de-risks adoption and demonstrates clear ROI through reduced administrative coding time and more consistent reporting.