Interpretation differences between radiologists and diagnostic errors are significant issues in daily radiology practice. An awareness of errors and their underlying causes can potentially increase the diagnostic performance and reduce individual harm. The aim of this paper is to review both the classification of errors and the underlying biases. Case-based examples are presented and discussed for each type of error and bias to provide greater clarity and understanding.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8056102 | PMC |
http://dx.doi.org/10.1186/s13244-021-00986-8 | DOI Listing |
Methods
January 2025
School of Computer Science and Engineering, Central South University, Changsha 410083, China; Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China.
Exploring the associations between microbes and drugs offers valuable insights into their underlying mechanisms. Traditional wet lab experiments, while reliable, are often time-consuming and labor-intensive, making computational approaches an attractive alternative. Existing similarity-based machine learning models for predicting microbe-drug associations typically rely on integrated similarities as input, neglecting the unique contributions of individual similarities, which can compromise predictive accuracy.
View Article and Find Full Text PDFPlants (Basel)
January 2025
School of Biological Sciences, University of Western Australia, Perth, WA 6009, Australia.
There was an error in the original publication [...
View Article and Find Full Text PDFJ Clin Med
January 2025
Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal.
An important impediment to the incorporation of artificial intelligence-based tools into healthcare is their association with so-called black box medicine, a concept arising due to their complexity and the difficulties in understanding how they reach a decision. This situation may compromise the clinician's trust in these tools, should any errors occur, and the inability to explain how decisions are reached may affect their relationship with patients. Explainable AI (XAI) aims to overcome this limitation by facilitating a better understanding of how AI models reach their conclusions for users, thereby enhancing trust in the decisions reached.
View Article and Find Full Text PDFMaterials (Basel)
January 2025
Laboratory for Heteroepitaxial Growth of Functional Materials & Devices, Department of Chemical & Biological Engineering, State University of New York (SUNY) at Buffalo, Buffalo, NY 14260, USA.
This study analyzes the calculation of the critical current density by means of Bean's critical state model, using the equation formulated by Gyorgy et al. and other similar equations derived from it reported in the literature. While estimations of using Bean's model are widely performed, improper use of different equations with different magnetic units and pre-factors leads to confusion and to significant errors in the reported values of .
View Article and Find Full Text PDFMedicina (Kaunas)
January 2025
Division of Allergy/Immunology, Department of Pediatrics, Jackson Memorial Holtz Children's Hospital, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
Inborn errors of immunity (IEIs), also known as primary immunodeficiencies, are a group of genetic disorders affecting the development and function of the immune system. While IEIs traditionally present with recurrent infections, an increasing number of cases manifest with early-onset severe atopy, including atopic dermatitis, food allergies, asthma, and allergic rhinitis-features that are often overlooked. This can lead to delayed diagnosis and treatment, which is crucial for IEI patients due to the risk of severe infections.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!