Front. Radiol. Frontiers in Radiology Front. Radiol. 2673-8740 Frontiers Media S.A. 10.3389/fradi.2025.1744006 Correction Correction: Diagnostic precision of a deep learning algorithm for the classification of non-contrast brain CT reports GüzelHamza Eren 1 * Conceptualization Data curation Formal analysis Funding acquisition Investigation Methodology Project administration Resources Software Supervision Validation Visualization Writing – original draft Writing – review & editing AşcıGöktuğ 2 * Conceptualization Formal analysis Writing – original draft Writing – review & editing DemirbilekOytun 3 Formal analysis Writing – original draft ÖzdemirTuğçe Doğa 1 Data curation Formal analysis Writing – original draft ErekliPelin Berfin 1 Data curation Formal analysis Writing – original draft Department of Radiology, İzmir City Hospital, İzmir, Türkiye School of Science and Technology, IE University, Segovia, Spain School of Management, Technical University of Munich, Munich, Germany Correspondence: Hamza Eren Güzel hamzaerenguzel@gmail.comGöktuğ Aşcıdrgoktugasci@gmail.com 24112025 2025 51744006 11112025 13112025 © 2025 Güzel, Aşcı, Demirbilek, Özdemir and Erekli. 2025Güzel, Aşcı, Demirbilek, Özdemir and Ereklihttps://creativecommons.org/licenses/by/4.0/ 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) and the copyright owner(s) 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. A Correction on Diagnostic precision of a deep learning algorithm for the classification of non-contrast brain CT reports By Güzel HE, Aşcı G, Demirbilek O, Özdemir TD and Erekli PB (2025). Frontiers in Radiology. 5:1509377. doi: 10.3389/fradi.2025.1509377 artificial intelligence report classification computed tomography deep learning noncontrast head CT section-at-acceptanceArtificial Intelligence in Radiology

In the published article, there was a mistake in the conclusion part of 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.

The original version of this article has been updated.

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Edited and Reviewed by: Maria Evelina Fantacci, University of Pisa, Italy