{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T11:13:36Z","timestamp":1788434016466,"version":"build-2803163510"},"reference-count":41,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Chest X-ray (CXR) imaging is crucial for diagnosing thoracic abnormalities; however, the rising demand burdens radiologists, particularly in resource-limited settings. Method: We used large-scale, diverse public CXR datasets with noisy labels to train general-purpose deep learning models (ResNet, DenseNet, EfficientNet, and DLAD-10) for multi-label classification of thoracic conditions. Uncertainty quantification was incorporated to assess model reliability. Performance was evaluated on both internal and external validation sets, with analyses of data scale, diversity, and fine-tuning effects. Result: EfficientNet achieved the highest overall area under the receiver operating characteristic curve (0.8944) with improved sensitivity and F1-score. Moreover, as training data volume increased\u2014particularly using multi-source datasets\u2014both diagnostic performance and generalizability were enhanced. Although larger datasets reduced predictive uncertainty, conditions such as tuberculosis remained challenging due to limited high-quality samples. Conclusions: General-purpose deep learning models can achieve robust CXR diagnostic performance when trained on large-scale, diverse public datasets despite noisy labels. However, further targeted strategies are needed for underrepresented conditions.<\/jats:p>","DOI":"10.3390\/diagnostics16010146","type":"journal-article","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T09:18:56Z","timestamp":1767345536000},"page":"146","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Leveraging Large-Scale Public Data for Artificial Intelligence-Driven Chest X-Ray Analysis and Diagnosis"],"prefix":"10.3390","volume":"16","author":[{"given":"Farzeen Khalid","family":"Khan","sequence":"first","affiliation":[{"name":"AI Laboratory, HealthHub Co., Ltd., Seoul 06524, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1312-8675","authenticated-orcid":false,"given":"Waleed Bin","family":"Tahir","sequence":"additional","affiliation":[{"name":"AI Laboratory, HealthHub Co., Ltd., Seoul 06524, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0382-5564","authenticated-orcid":false,"given":"Mu Sook","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Radiology, Keimyung University Dongsan Hospital, Daegu 24601, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin Young","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Radiology, Keimyung University Dongsan Hospital, Daegu 24601, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7189-4000","authenticated-orcid":false,"given":"Shi Sub","family":"Byon","sequence":"additional","affiliation":[{"name":"AI Laboratory, HealthHub Co., Ltd., Seoul 06524, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sun-Woo","family":"Pi","sequence":"additional","affiliation":[{"name":"Division of AI and Computer Engineering, Kyonggi University, Suwon 16227, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Byoung-Dai","family":"Lee","sequence":"additional","affiliation":[{"name":"Division of AI and Computer Engineering, Kyonggi University, Suwon 16227, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1378\/chest.10-1302","article-title":"Interpretation of plain chest roentgenogram","volume":"141","author":"Raoof","year":"2012","journal-title":"Chest"},{"key":"ref_2","unstructured":"WHO (World Health Organization) (2021). 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