Developing effective clinical reasoning is central to health professions education. Learning by concordance (LbC) is an on-line educational strategy that makes learners practice reasoning competency in case-based clinical situations. The questions asked are similar to those professionals ask themselves in their practice and participant answers are compared to those of a reference panel. When participants answer the questions, they receive an automated feedback that is two-fold as they see (1) how the panelists respond and (2) justifications each panelist gives for their answer. This provides rich contextual knowledge about the situation, supplemented by a synthesis summarizing crucial points. As many educators in the health sciences are engaging in introducing innovative approaches, many consider building LbC learning modules. Elaborating, designing and implementing a LbC tool remain a challenge. This AMEE Guide describes the steps and elements to be considered when designing a LbC tool, drawing on examples from distinct health professions: medicine, nursing, physiotherapy, and dentistry. Specifically, the following elements will be discussed: (1) LbC theoretical underpinnings; (2) principles of LbC questioning; (3) goals of the concordance-based activity; (4) nature of reasoning tasks; (5) content/levels of complexity; (6) reference panel; (7) feedback/synthesis messages; (8) on-line learning platforms.
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ACS Omega
January 2025
School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China.
Accurate drug-target binding affinity (DTA) prediction is crucial in drug discovery. Recently, deep learning methods for DTA prediction have made significant progress. However, there are still two challenges: (1) recent models always ignore the correlations in drug and target data in the drug/target representation process and (2) the interaction learning of drug-target pairs always is by simple concatenation, which is insufficient to explore their fusion.
View Article and Find Full Text PDFJ Alzheimers Dis
January 2025
Department of Quantitative Health Sciences, University of Hawaii John A. Burns School of Medicine, Honolulu, HI, USA.
Background: Survival after an Alzheimer's disease (AD) diagnosis is vital for patients, their families, caregivers, and healthcare providers. Hawaii, known for its diverse ethnic population, exhibits significant racial health disparities.
Objective: This study examined racial/ethnic and socioeconomic disparities in AD survival in Hawaii and developed machine learning models to predict overall survival using Hawaii Medicare data.
Comput Med Imaging Graph
January 2025
Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, South Korea; Department of Family Medicine, Seoul National University Hospital, Seoul, South Korea. Electronic address:
This study introduces the Deep Learning-based Cardiovascular Disease Incident (DL-CVDi) score, a novel biomarker derived from routine abdominal CT scans, optimized to predict cardiovascular disease (CVD) risk using deep survival learning. CT imaging, frequently used for diagnosing various conditions, contains opportunistic biomarkers that can be leveraged beyond their initial diagnostic purpose. Using a Cox proportional hazards-based survival loss, the DL-CVDi score captures complex, non-linear relationships between anatomical features and CVD risk.
View Article and Find Full Text PDFJ Eur Acad Dermatol Venereol
January 2025
Pathology Department, IHP Group, Nantes, France.
Background: There is a need to improve risk stratification of primary cutaneous melanomas to better guide adjuvant therapy. Taking into account that haematoxylin and eosin (HE)-stained tumour tissue contains a huge amount of clinically unexploited morphological informations, we developed a weakly-supervised deep-learning approach, SmartProg-MEL, to predict survival outcomes in stages I to III melanoma patients from HE-stained whole slide image (WSI).
Methods: We designed a deep neural network that extracts morphological features from WSI to predict 5-y overall survival (OS), and assign a survival risk score to each patient.
Cureus
December 2024
Medical Education, University of South Florida Morsani College of Medicine, Tampa, USA.
Background AI language models have been shown to achieve a passing score on certain imageless diagnostic tests of the USMLE. However, they have failed certain specialty-specific examinations. This suggests there may be a difference in AI ability by medical topic or question difficulty.
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