Abstract Automated analysis of bedside chest radiographs remains challenging due to limited large-scale datasets with expert annotations and standardized severity grading. We provide TAIX-Ray, a comprehensive dataset of 215,381 bedside chest radiographs collected from 47,724 intensive care unit patients (30,306 male, 17,418 female, median age 68 years) collected over 14 years (01/2010-12/2023) at the University Hospital Aachen, Germany. During routine clinical reporting, 134 trained radiologists provided structured, itemized reports using a standardized template. They systematically assessed eight pathological findings: heart size (cardiomegaly), pulmonary congestion, pleural effusion (left/right), pulmonary opacities (left/right), and atelectasis (left/right) using a five-point ordinal severity scale (absent, questionable, mild, moderate, severe). The dataset includes (i) bedside chest radiographs (anteroposterior projections), (ii) structured, itemized reports, (iii) patient demographics (age and sex), and (iv) the temporal metadata. To facilitate immediate research adoption, we provide a baseline transformer model, implementation code, and predefined data splits, ensuring reproducible benchmarking. This resource enables the development of clinical AI models for automated pathology detection and severity assessment in critical care settings.
Truhn et al. (2026) studied this question.