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Use of evidence-based health professions education: Attitudes and practices of academic leaders.
PLoS One
January 2025
Department of Conservative Dental Sciences, College of Dentistry, Qassim University, Buraidah, Qassim, Saudi Arabia.
Purpose: The objective of this study was to explore the attitudes, practices, supports, and barriers of academic leaders regarding the use of Evidence-Based Health Professional Education (EBHPE).
Methods: A cross-sectional survey was conducted on 79 faculty members in leadership positions, from four different undergraduate colleges at Qassim University. A pre-validated questionnaire was distributed electronically.
Large-Scale Validation of the Feasibility of GPT-4 as a Proofreading Tool for Head CT Reports.
Radiology
January 2025
From the Departments of Biomedical Systems Informatics (S.K., Jaewoong Kim, C.H., D.Y.) and Neurology (Joonho Kim, J.Y.), Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea; Department of Radiology, Central Draft Physical Examination Office of Military Manpower Administration, Daegu, Republic of Korea (D.K.); Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science (H.J.S. Y.K., S.J.), and Center for Digital Health (H.J.S., D.Y.), Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Republic of Korea; Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea (S.H.L.); Departments of Radiology (M.H.) and Neurology (S.J.L.), Ajou University Hospital, Ajou University School of Medicine, Suwon, Republic of Korea; and Institute for Innovation in Digital Healthcare, Severance Hospital, Seoul, Republic of Korea (D.Y.).
Background The increasing workload of radiologists can lead to burnout and errors in radiology reports. Large language models, such as OpenAI's GPT-4, hold promise as error revision tools for radiology. Purpose To test the feasibility of GPT-4 use by determining its error detection, reasoning, and revision performance on head CT reports with varying error types and to validate its clinical utility by comparison with human readers.
View Article and Find Full Text PDFType 2 diabetes: a sacrifice program handling energy surplus.
Life Metab
December 2024
Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai 200233, China.
Type 2 diabetes mellitus (T2DM) is closely associated with obesity, while interactions between the two diseases remain to be fully elucidated. To this point, we offer this perspective to introduce a set of new insights into the interpretation of T2DM spanning the etiology, pathogenesis, and treatment approaches. These include a definition of T2DM as an energy surplus-induced diabetes characterized by the gradual decline of β cell insulin secretion function, which ultimately aims to prevent the onset of severe obesity through mechanisms of weight loss.
View Article and Find Full Text PDFLearning From Full Characterization of HIV Proviruses in People Receiving Long-Acting Cabotegravir/Rilpivirine With a History of Replication on the Antiretroviral Classes.
Open Forum Infect Dis
January 2025
CHU d'Orléans, Orléans, France.
Background: To better understand factors associated with virologic response, we retrospectively characterized the HIV proviruses of 7 people with HIV who received long-acting cabotegravir/rilpivirine (CAB/RPV-LA) and were selected according to the following criteria: virologic control achieved despite a history of viral replication on 1 or both corresponding antiretroviral classes (n = 6) and virologic failure (VF) after CAB/RPV-LA initiation (n = 1).
Methods: Last available blood samples before the initiation of CAB/RPV-LA were analyzed retrospectively. Near full-length HIV DNA genome haplotypes were inferred from Nanopore sequencing by the in vivo Genome Diversity Analyzer to search for archived drug resistance mutations (DRMs) and evaluate the frequency and intactness of proviruses harboring DRMs.
Current state and promise of user-centered design to harness explainable AI in clinical decision-support systems for patients with CNS tumors.
Front Radiol
January 2025
Department of Biostatistics & Informatics, Colorado School of Public Health, Aurora, CO, United States.
In neuro-oncology, MR imaging is crucial for obtaining detailed brain images to identify neoplasms, plan treatment, guide surgical intervention, and monitor the tumor's response. Recent AI advances in neuroimaging have promising applications in neuro-oncology, including guiding clinical decisions and improving patient management. However, the lack of clarity on how AI arrives at predictions has hindered its clinical translation.
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