Diagnostic errors remain relatively understudied and underappreciated. They are particularly concerning in the intensive care unit, where they are more likely to result in harm to patients. There is a lack of consensus on the definition of diagnostic error, and current methods to quantify diagnostic error have numerous limitations as noted in the sentinel report by the National Academy of Medicine. Although definitive definition and measurement remain elusive goals, increasing our understanding of diagnostic error is crucial if we are to make progress in reducing the incidence and harm caused by errors in diagnosis.
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http://dx.doi.org/10.1016/j.ccc.2021.08.001 | DOI Listing |
J Med Internet Res
January 2025
Department of Computer Science and Software Engineering, United Arab Emirates University, Al Ain, United Arab Emirates.
Background: Neuroimaging segmentation is increasingly important for diagnosing and planning treatments for neurological diseases. Manual segmentation is time-consuming, apart from being prone to human error and variability. Transformers are a promising deep learning approach for automated medical image segmentation.
View Article and Find Full Text PDFCodas
January 2025
Programa de Pós-Graduação em Fonoaudiologia, Universidade Estadual Paulista "Júlio de Mesquita Filho" - UNESP - Marília (SP), Brasil.
Purpose: To investigate whether there is a difference in the classification of speech hypernasality by inexperienced listeners using different ordinal scales; to verify the agreement of the listeners in the analyses when using these scales; and to verify whether the order in which the scales are presented influences the results.
Methods: Twenty Speech-Language Pathology students classified the degrees of hypernasality of 40 (oral) samples from patients with cleft lip and palate. Ten performed the classifications using a 4-point scale (absent, mild, moderate, and severe) and, after two weeks, using a 3-point scale (absent, slightly hypernasal, and very hypernasal).
J Clin Immunol
January 2025
Department of Health Systems & Implementation Science, Virginia Tech Carilion School of Medicine, Roanoke, VA, USA.
Acta Paediatr
January 2025
Department of Pathology, Sourasky Medical Center, Tel Aviv, Israel.
Aim: Diagnostic error can result in the appendectomy of a normal appendix, commonly known as negative appendectomy (NA). Missed appendicitis (MA) is related to a poor outcome. The aim of this study was to determine whether there are factors in presentation associated with NA or MA.
View Article and Find Full Text PDFCureus
December 2024
Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, IRN.
Background Orthodontic diagnostic workflows often rely on manual classification and archiving of large volumes of patient images, a process that is both time-consuming and prone to errors such as mislabeling and incomplete documentation. These challenges can compromise treatment accuracy and overall patient care. To address these issues, we propose an artificial intelligence (AI)-driven deep learning framework based on convolutional neural networks (CNNs) to automate the classification and archiving of orthodontic diagnostic images.
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