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Across all 45 datasets, we found substantial redundancy (51% case reuse) and a Western\/geographic skew (75.3% from North America and Europe). A bias assessment was performed on the 22 datasets with more than 100 cases; within this subset, the most prevalent high-risk categories were racial bias (with a score of 16 out of 22) and selection bias (with a score of 15 out of 22), both of which may undermine model generalizability across diverse healthcare environments\u2014particularly in resource-limited settings. To address these challenges, we propose targeted strategies for dataset improvement, including multi-institutional collaboration, adoption of standardized protocols, and deliberate inclusion of diverse patient populations and imaging technologies. These efforts are crucial in supporting the development of more equitable and clinically robust AI models for abdominal imaging.<\/jats:p>","DOI":"10.1371\/journal.pdig.0001567","type":"journal-article","created":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T17:35:36Z","timestamp":1786383336000},"page":"e0001567","update-policy":"https:\/\/doi.org\/10.1371\/journal.pdig.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["State of abdominal CT datasets: A critical review of bias, clinical relevance, and real-world applicability"],"prefix":"10.1371","volume":"5","author":[{"given":"Saeide","family":"Danaei","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zahra","family":"Dehghanian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elahe","family":"Meftah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nariman","family":"Naderi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9295-9283","authenticated-orcid":true,"given":"Seyed Amir Ahmad","family":"Safavi-Naini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Faeze","family":"Khorasanizade","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9835-4493","authenticated-orcid":true,"given":"Hamid R.","family":"Rabiee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,8,10]]},"reference":[{"issue":"80","key":"pdig.0001567.ref001","doi-asserted-by":"crossref","DOI":"10.15557\/JoU.2020.0008","article-title":"Indications for abdominal imaging: when and what to choose?","volume":"20","author":"C Caraiani","year":"2020","journal-title":"J Ultrason"},{"key":"pdig.0001567.ref002","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1016\/j.jacr.2017.12.026","article-title":"Artificial intelligence, machine learning in radiology: opportunities, challenges, pitfalls, criteria for success","volume":"15","author":"JH Thrall","year":"2018","journal-title":"J Am Coll Radiol"},{"issue":"8","key":"pdig.0001567.ref003","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1038\/s41568-018-0016-5","article-title":"Artificial intelligence in radiology","volume":"18","author":"A Hosny","year":"2018","journal-title":"Nat Rev Cancer"},{"issue":"11","key":"pdig.0001567.ref004","doi-asserted-by":"crossref","first-page":"1814","DOI":"10.3390\/tomography10110133","article-title":"Evolving, novel applications of artificial intelligence in abdominal imaging","volume":"10","author":"MR Loper","year":"2024","journal-title":"Tomography"},{"issue":"10","key":"pdig.0001567.ref005","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1038\/s42256-019-0099-z","article-title":"Clinically applicable deep learning framework for organs at risk delineation in CT images","volume":"1","author":"H Tang","year":"2019","journal-title":"Nat Mach Intell"},{"issue":"1","key":"pdig.0001567.ref006","article-title":"Sharing data is essential for the future of AI in Medical Imaging","volume":"6","author":"LC Bell","year":"2024","journal-title":"Radiol Artif Intell"},{"issue":"1","key":"pdig.0001567.ref007","doi-asserted-by":"crossref","first-page":"1471","DOI":"10.3233\/ADR-240129","article-title":"Medical imaging datasets, preparation, and availability for artificial intelligence in medical imaging","volume":"8","author":"A Alabduljabbar","year":"2024","journal-title":"J Alzheimers Dis Rep"},{"issue":"2","key":"pdig.0001567.ref008","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1038\/s42256-024-00797-8","article-title":"A causal perspective on dataset bias in machine learning for medical imaging","volume":"6","author":"C Jones","year":"2024","journal-title":"Nat Mach Intell"},{"key":"pdig.0001567.ref009","doi-asserted-by":"crossref","unstructured":"Ko\u00e7ak B, Ponsiglione A, Stanzione A, Bluethgen C, Santinha J, Ugga L. 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