CORRECTION article
Front. Radiol.
Sec. Artificial Intelligence in Radiology
Correction: Diagnostic precision of a deep learning algorithm for the classification of non-contrast brain CT reports
Provisionally accepted
- 1Department of Radiology, İzmir City Hospital, İzmir, Türkiye, İzmir, Türkiye
- 2School of Science and Technology, IE University, Segovia, Spain, Segovia, Spain
- 3School of Management, Technical University of Munich, Munich, Germany, Munich, Germany
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In the abstract, [This study revealed decreased diagnostic accuracy of an AI decision support system (DSS) at our institution. Despite extensive evaluation, we were unable to identify the source of this discrepancy, raising concerns about the generalizability of these tools with indeterminate failure modes. These results further highlight the need for standardized study design to allow for rigorous and reproducible site-to-site comparison of emerging deep learning technologies.]. This has been corrected to read: [The proposed deep learning algorithm demonstrated high diagnostic accuracy, sensitivity, and specificity in classifying non-contrast brain CT reports. These results indicate the feasibility of automated identification of critical cases, which may support workflow efficiency and timely patient management in radiology practice. ]In the published article, there was a mistake in the conclusion part of the abstract which does not reflect the study conclusion accurately. I would kindly ask if the abstract concludion could
Keywords: artificial intelligence, report classification, computed tomography, deep learning, noncontrast head CT
Received: 11 Nov 2025; Accepted: 13 Nov 2025.
Copyright: © 2025 Güzel, Aşcı, Demirbilek, Özdemir and Erekli. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Hamza Eren Güzel, hamzaerenguzel@gmail.com
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