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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.<\/jats:p>","DOI":"10.1038\/s41597-026-07271-7","type":"journal-article","created":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T09:48:17Z","timestamp":1776764897000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A comprehensive bedside chest radiography dataset with structured, itemized and graded radiologic reports"],"prefix":"10.1038","volume":"13","author":[{"given":"Daniel","family":"Truhn","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Geiger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert","family":"Siepmann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marc Sebastian","family":"von der St\u00fcck","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Keno Kyrill","family":"Bressem","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3730-5348","authenticated-orcid":false,"given":"Jakob Nikolas","family":"Kather","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christiane","family":"Kuhl","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7413-2570","authenticated-orcid":false,"given":"Gustav","family":"M\u00fcller-Franzes","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5267-9962","authenticated-orcid":false,"given":"Sven","family":"Nebelung","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,21]]},"reference":[{"key":"7271_CR1","doi-asserted-by":"publisher","first-page":"S62","DOI":"10.1016\/j.jacr.2021.01.017","volume":"18","author":"AT Laroia","year":"2021","unstructured":"Laroia, A. T. et al. ACR Appropriateness Criteria\u00ae Intensive Care Unit Patients. Journal of the American College of Radiology 18, S62\u2013S72 (2021).","journal-title":"Journal of the American College of Radiology"},{"key":"7271_CR2","doi-asserted-by":"publisher","first-page":"102125","DOI":"10.1016\/j.media.2021.102125","volume":"72","author":"E \u00c7all\u0131","year":"2021","unstructured":"\u00c7all\u0131, E., Sogancioglu, E., Van Ginneken, B., Van Leeuwen, K. G. & Murphy, K. Deep learning for chest X-ray analysis: A survey. Medical Image Analysis 72, 102125 (2021).","journal-title":"Medical Image Analysis"},{"key":"7271_CR3","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.220510","volume":"307","author":"F Khader","year":"2023","unstructured":"Khader, F. et al. Artificial Intelligence for Clinical Interpretation of Bedside Chest Radiographs. Radiology 307, e220510 (2023).","journal-title":"Radiology"},{"key":"7271_CR4","doi-asserted-by":"publisher","first-page":"159","DOI":"10.3390\/diagnostics13010159","volume":"13","author":"A Ait Nasser","year":"2023","unstructured":"Ait Nasser, A. & Akhloufi, M. A. A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using Radiography. Diagnostics 13, 159 (2023).","journal-title":"Diagnostics"},{"key":"7271_CR5","doi-asserted-by":"publisher","unstructured":"Wang, X. et al. ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 3462\u20133471, https:\/\/doi.org\/10.1109\/CVPR.2017.369 (IEEE, Honolulu, HI, 2017).","DOI":"10.1109\/CVPR.2017.369"},{"key":"7271_CR6","doi-asserted-by":"publisher","unstructured":"Zhang, X., Acosta, J. N., Miller, J., Huang, O. & Rajpurkar, P. ReXGradient-160K: A Large-Scale Publicly Available Dataset of Chest Radiographs with Free-text Reports. Preprint at https:\/\/doi.org\/10.48550\/ARXIV.2505.00228 (2025).","DOI":"10.48550\/ARXIV.2505.00228"},{"key":"7271_CR7","doi-asserted-by":"publisher","first-page":"101797","DOI":"10.1016\/j.media.2020.101797","volume":"66","author":"A Bustos","year":"2020","unstructured":"Bustos, A., Pertusa, A., Salinas, J.-M. & De La Iglesia-Vay\u00e1, M. PadChest: A large chest x-ray image dataset with multi-label annotated reports. Medical Image Analysis 66, 101797 (2020).","journal-title":"Medical Image Analysis"},{"key":"7271_CR8","doi-asserted-by":"publisher","unstructured":"Irvin, J. et al. CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison. 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J.N.K. declares consulting services for Bioptimus, France; Panakeia, UK; AstraZeneca, UK; and MultiplexDx, Slovakia. Furthermore, he holds shares in StratifAI, Germany, Synagen, Germany, and Ignition Lab, Germany; has received an institutional research grant by GSK; and has received honoraria by AstraZeneca, Bayer, Daiichi Sankyo, Eisai, Janssen, Merck, MSD, BMS, Roche, Pfizer, and Fresenius. K.K.B. received speaker fees from Canon Medical Systems Cooperations and GE Healthcare. All other authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"632"}}