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Traditional score-based peer review has come under scrutiny in recent years, as studies have demonstrated it to be generally ineffective at improving quality. Many practices and programs are transitioning to a peer learning model to replace or supplement traditional peer review. Peer learning differs from traditional score-based peer review in that the emphasis is on sharing learning opportunities and creating an environment that fosters discussion of errors in a nonpunitive forum with the goal of improved patient care. Creating a just culture is central to fostering successful peer learning. In a just culture, mistakes can be discussed without shame or fear of retribution and the focus is on systems improvement rather than individual blame. Peer learning, as it pertains to breast imaging, can occur in many forms and venues. Examples of the various formats in which peer learning can occur include through individual colleague interaction, as well as divisional, multidisciplinary, department-wide, and virtual conferences, and with the assistance of artificial intelligence. Incorporating peer learning into the practice of breast imaging aims to reduce delayed diagnoses of breast cancer and optimize patient care.
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http://dx.doi.org/10.1093/jbi/wbab043 | DOI Listing |
Sci Robot
September 2025
College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
According to productive failure (PF) theory, experiencing failure during problem-solving can enhance students' knowledge acquisition in subsequent instruction. However, challenging students with problems beyond their current capabilities may strain their skills, prior knowledge, and emotional well-being. To address this, we designed a social robot-assisted teaching activity in which students observed a robot's unsuccessful problem-solving attempts, offering a PF-like preparatory effect without requiring direct failure.
View Article and Find Full Text PDFHealth Expect
October 2025
Department of Social Work and Social Administration, The University of Hong Kong, Hong Kong, China.
Background: Serving as peer supporters in later life has been linked to a greater sense of purpose and meaning in life. How the wisdom of older adults could be leveraged to improve the implementation of peer support work, however, has rarely been considered. We aimed to examine the perspectives of peer supporters in this study, including the challenges they encountered in practice and the strategies they developed to navigate their roles.
View Article and Find Full Text PDFFam Cancer
September 2025
School of Social Policy and Practice, University of Pennsylvania, Philadelphia, USA.
Li-Fraumeni syndrome (LFS) is an early-onset cancer syndrome caused by pathogenic germline TP53 variants. Adolescents and young adults (AYAs) with LFS may have challenges navigating new romantic partnerships given the significant effects of LFS on multiple life domains that also affect partners (e.g.
View Article and Find Full Text PDFRadiol Artif Intell
September 2025
Department of Radiology, Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, No. 197 Ruijin Er Road, Shanghai 200025, China.
Purpose To assess the effectiveness of an explainable deep learning (DL) model, developed using multiparametric MRI (mpMRI) features, in improving diagnostic accuracy and efficiency of radiologists for classification of focal liver lesions (FLLs). Materials and Methods FLLs ≥ 1 cm in diameter at mpMRI were included in the study. nn-Unet and Liver Imaging Feature Transformer (LIFT) models were developed using retrospective data from one hospital (January 2018-August 2023).
View Article and Find Full Text PDFFront Rehabil Sci
August 2025
Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, CA, United States.
Introduction: Spinal cord injury (SCI) presents a significant burden to patients, families, and the healthcare system. The ability to accurately predict functional outcomes for SCI patients is essential for optimizing rehabilitation strategies, guiding patient and family decision making, and improving patient care.
Methods: We conducted a retrospective analysis of 589 SCI patients admitted to a single acute rehabilitation facility and used the dataset to train advanced machine learning algorithms to predict patients' rehabilitation outcomes.