arXiv Computer Vision
An Agentic AI Workflow for Liver Lesion Segmentation and Structured Radiology Reporting
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AMPLIFAI: A Multiphase CT Dataset for Benchmarking Clinical Reasoning in LI-RADS Assessment of Liver Lesions
The paper introduces AMPLIFAI, the first public dataset of multiphase abdominal CT scans annotated with LI-RADS categories and segmented for three key LI-RADS features: arterial phase hyperenhancement, washout, and enhancing capsule. It outlines the dataset’s composition, curation process, and annotation pipeline following the Datasheets for Datasets format to promote transparency and reproducibility. The dataset aims to support the development of AI models for automated hepatocellular carcinoma diagnosis using the biopsy‑free, imaging‑based LI‑RADs framework.
arXiv AI
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INFORM-CT: INtegrating LLMs and VLMs FOR Incidental Findings Management in Abdominal CT
arXiv:2512. 14732v3 Announce Type: replace-cross Abstract: Incidental findings in CT scans, though often benign, can have significant clinical implications and should be reported following established guidelines.
arXiv AI
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TIAgent: From Natural Language to Grounded Agentic Workflows for Computational Pathology
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arXiv AI
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RadAgent: A tool-using AI agent for stepwise interpretation of chest computed tomography
arXiv:2604. 15231v2 Announce Type: replace Abstract: Vision-language models (VLM) have markedly advanced AI-driven interpretation and reporting of complex medical imaging, such as computed tomography (CT).
arXiv Computer Vision
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MedPAO-VL: Interpretable and Protocol-Compliant Agent for Structured Radiology Report Generation
MedPAO‑VL is a multimodal agent that generates structured radiology reports from chest X‑rays using an explainable plan‑act‑observe loop. The system first interprets the image with a vision module that captures diagnostic uncertainty, then iteratively refines the report by correcting semantic discrepancies to better match visual findings. Evaluation shows improved clinical correctness and structural compliance, with expert ratings of 3.5/5 for accuracy and 4.3/5 for structure.
Hugging Face Trending Papers
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MedPAO-VL: Interpretable and Protocol-Compliant Agent for Structured Radiology Report Generation
Despite the remarkable generalization capabilities of Large Language Models, generating clinically accurate and structured radiology reports from medical images remains a significant challenge. Curren...