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Artificial intelligence and machine learning capabilities in the detection of acute scaphoid fracture: a critical review.
J Hand Surg Eur Vol
January 2025
Clinical Scientific Computing, Guy's and St Thomas' NHS Foundation Trust, London, UK.
This paper discusses the current literature surrounding the potential use of artificial intelligence and machine learning models in the diagnosis of acute obvious and occult scaphoid fractures. Current studies have notable methodological flaws and are at high risk of bias, precluding meaningful comparisons with clinician performance (the current reference standard). Specific areas should be addressed in future studies to help advance the meaningful and clinical use of artificial intelligence for radiograph interpretation.
View Article and Find Full Text PDFRandom Survival Forest Machine Learning for the Prediction of Cardiovascular Events Among Patients With a Measured Lipoprotein(a) Level: A Model Development Study.
Circ Genom Precis Med
January 2025
Department of Medicine, Division of Cardiology (M.P., N.J.P., N.P.S.), Duke University, Durham, NC.
Background: Established risk models may not be applicable to patients at higher cardiovascular risk with a measured Lp(a) (lipoprotein[a]) level, a causal risk factor for atherosclerotic cardiovascular disease.
Methods: This was a model development study. The data source was the Nashville Biosciences Lp(a) data set, which includes clinical data from the Vanderbilt University Health System.
Artificial Intelligence-Based Detection and Numbering of Dental Implants on Panoramic Radiographs.
Clin Implant Dent Relat Res
February 2025
SEMRUK Technology Inc., Cumhuriyet Teknokent, Sivas, Turkiye.
Objectives: This study aimed to develop an artificial intelligence (AI)-based deep learning model for the detection and numbering of dental implants in panoramic radiographs. The novelty of this model lies in its ability to both detect and number implants, offering improvements in clinical decision support for dental implantology.
Materials And Methods: A retrospective dataset of 32 585 panoramic radiographs, collected from patients at Sivas Cumhuriyet University between 2014 and 2024, was utilized.
Editorial: Innovative educational methods for Family Medicine and Primary Care training.
Afr J Prim Health Care Fam Med
December 2024
Department of Medical Education, Faculty of Medicine, University of Botswana, Gaborone.
Graduate perceptions of their interprofessional practice: Lessons for undergraduate training.
Afr J Prim Health Care Fam Med
December 2024
Division of Rural Health (Ukwanda), Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa; and, Department of Health Professions Education, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town.
Background: Interprofessional education (IPE) during undergraduate training (UGT) is considered important for new graduates to collaborate inter-professionally. There are, however, well-documented workplace challenges that hinder their involvement in interprofessional collaborative practice (IPCP) such as professional hierarchy, poor role clarification and communication challenges.
Aim: This article explores graduates' perceptions of the value rural undergraduate IPE had on their IPCP during their first year of work.
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