Agentic AI links liver scans with detailed radiology reports
An Agentic AI Workflow for Liver Lesion Segmentation and Structured Radiology Reporting
Computer Vision and Pattern RecognitionMachine Learning
Summary
Doctors use images of the liver to find and describe unusual spots called lesions, but turning these images into easy-to-understand reports is tricky. The authors created a step-by-step AI system that first outlines the liver and spots on scans, then measures their size and location, and finally writes structured reports people can trust. The AI double-checks numbers to make sure everything matches the original images. This helps connect detailed image analysis with clear reports that doctors can rely on.
What this means in practice
- •For radiology departments: Automatically generate structured liver lesion reports from medical scans with accurate measurement verification.
- •For medical imaging software developers: Build integrated AI tools that connect segmentation models with report-generation models to produce traceable imaging findings.
Authors
Fatma Ahmed Mansour, Rahatara Ferdousi, Manar Fathy Elshenawy
Abstract
Despite advances in automated liver lesion segmentation, a gap remains between image-derived predictions and clinically interpretable radiology reports. Bridging this gap requires quantitative measurements that are both reproducible and traceable. We present an evidence-grounded agentic AI framework that connects liver and lesion segmentation with deterministic quantification and structured reporting. TotalSegmentator-based nnU-Net models delineate the liver, Couinaud segments, and lesions, while computational tools extract volumes, dimensions, counts, and anatomical locations. A large language model (LLM) organizes these measurements into a predefined reporting schema, with numerical consistency checks against source measurements. On 51 LiTS cases, the segmentation stage achieved a liver Dice score of 0.947, lesion sensitivity of 75.6%, and positive predictive value of 68.9%. Exploratory screening of 25,494 MERLIN reports identified 10,208 (40.0%) with liver-related abnormalities. By connecting quantitative imaging evidence with agentic report generation, this work takes a step toward human-centered medical AI that prioritizes traceability and clinical relevance.