← Back to all news
arXiv AI By Harshil Lodhiya

Clinical Knowledge Graphs for Chest X-Ray Device Reasoning

Read the original on arXiv AI →

The paper introduces an uncertainty‑aware clinical knowledge graph for chest X‑ray device reasoning, capturing device instances, tip estimates, placement assessments, provenance, report events, and temporal links as interconnected evidence. The graph builder processes 30,083 studies from 3,255 patients, producing 914,632 evidence nodes and 884,549 typed relationships, while preserving detailed uncertainty and provenance information for each predicted device. The authors also outline typed data contracts, uncertainty representations, abstention rules, report‑image grounding, and longitudinal query mechanisms, though the current analysis is post‑hoc descriptive and does not yet demonstrate clinical utility.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv AI
Aug 21

MKG-CARE: Case-Aware Reasoning with Multimodal Knowledge Graphs for Explainable Medical Image Diagnosis

arXiv:2605. 22547v3 Announce Type: replace-cross Abstract: Medical image diagnosis has achieved significant progress with deep learning, yet existing methods often rely on isolated visual evidence and lack the ability to effectively leverage similar cases and external knowledge.

By Yiming Xu, Yixuan Liu, Yuhang Zhang, Ling Zheng, Yihan Wang, Qi Song
multimodalbenchmarkssafety
More like this →
arXiv Computer Vision
Sep 21

Graph-Augmented Topological Internalization with Dual-Stream Classifiers for Medical Report Generation

The paper introduces GDMRG, a Graph-Augmented Dual-Stream Medical Report Generation framework that incorporates a Topological Knowledge Internalization module using a Graph Convolutional Network to encode disease co-occurrence priors. It employs a dual-stream classifier—one branch generating diagnostic prompts under topological constraints and an auxiliary branch dynamically calibrating decision boundaries for imbalanced samples—alongside a Diagnosis-Guided Spatial Attention mechanism to align visual features with clinical semantics. Experiments on MIMIC-CXR show competitive clinical efficacy and natural language fluency, with strong zero-shot performance on IU X-Ray.

By Moyu Tang, Shangkun Sima, Chupei Tang, Junxiao Kong, Di Wang, Tianchi Lu
safety
More like this →
arXiv AI
Jun 16

XMedFusion: A Knowledge-Guided Multimodal Perception and Reasoning Framework for Autonomous Medical Systems

arXiv:2606. 14766v1 Announce Type: cross Abstract: Autonomous medical and robotic systems increasingly rely on intelligent perception and reasoning capabilities to interpret visual data and support clinical decision making.

By Hamza Riaz, Arham Haroon, Maha Baig, Muhammad Dawood Rizwan, Muhammad Naseer Bajwa, Muhammad Moazam Fraz
llmsagentsroboticsmultimodal
More like this →
arXiv Machine Learning
Jul 30

Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance

arXiv:2607. 26333v1 Announce Type: cross Abstract: Chest X-ray (CXR) machine learning relies heavily on automated evaluation using reference standards that aim to approximate clinical judgment.

By Panagiotis Fytas, Ian Selby, Clemens Karner, Judith Babar, Simon Baker, Jake Beckford, Timothy J. Sadler, Shahab Shahipasand, Arthikkaa Thavakumar, John Li Chen, Alex Sawer, Michael Roberts, Jonathan Weir-McCall, J. H. F. Rudd, Carola-Bibiane Sch\"onlieb, Anna Korhonen, Anna Breger
llmsfine-tuningmultimodalsafety
More like this →