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The Effect of Nursing Students' Artificial Intelligence Anxiety on Their Knowledge of Robotic Surgery: The Mediating Role of Individual Innovativeness.
J Eval Clin Pract
February 2025
Surgical Nursing Department, Faculty of Health Sciences, Afyonkarahisar Health Sciences University, Afyonkarahisar, Turkey.
Aims: This study aims of determine the mediating role of individual innovativeness in the effect of nursing students' artificial intelligence anxiety on their robotic surgery knowledge level.
Design: This study was cross-sectional type.
Methods: It was conducted with 391 students.
Continuous Instrument Tracking in a Cerebral Corticectomy Ex Vivo Calf Brain Simulation Model: Face and Content Validation.
Oper Neurosurg (Hagerstown)
July 2024
Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal , Quebec , Canada.
Background And Objectives: Subpial corticectomy involving complete lesion resection while preserving pial membranes and avoiding injury to adjacent normal tissues is an essential bimanual task necessary for neurosurgical trainees to master. We sought to develop an ex vivo calf brain corticectomy simulation model with continuous assessment of surgical instrument movement during the simulation. A case series study of skilled participants was performed to assess face and content validity to gain insights into the utility of this training platform, along with determining if skilled and less skilled participants had statistical differences in validity assessment.
View Article and Find Full Text PDFArtificial Intelligence-Guided Lung Ultrasound by Nonexperts.
JAMA Cardiol
January 2025
Department of Emergency Medicine, Rush University Medical Center, Chicago, Illinois.
Importance: Lung ultrasound (LUS) aids in the diagnosis of patients with dyspnea, including those with cardiogenic pulmonary edema, but requires technical proficiency for image acquisition. Previous research has demonstrated the effectiveness of artificial intelligence (AI) in guiding novice users to acquire high-quality cardiac ultrasound images, suggesting its potential for broader use in LUS.
Objective: To evaluate the ability of AI to guide acquisition of diagnostic-quality LUS images by trained health care professionals (THCPs).
Speech Technology for Automatic Recognition and Assessment of Dysarthric Speech: An Overview.
J Speech Lang Hear Res
January 2025
Centre for Language Studies, Radboud University, Nijmegen, the Netherlands.
Purpose: In this review article, we present an extensive overview of recent developments in the area of dysarthric speech research. One of the key objectives of speech technology research is to improve the quality of life of its users, as evidenced by the focus of current research trends on creating inclusive conversational interfaces that cater to pathological speech, out of which dysarthric speech is an important example. Applications of speech technology research for dysarthric speech demand a clear understanding of the acoustics of dysarthric speech as well as of speech technologies, including machine learning and deep neural networks for speech processing.
View Article and Find Full Text PDFDifferences in technical and clinical perspectives on AI validation in cancer imaging: mind the gap!
Eur Radiol Exp
January 2025
Computational Clinical Imaging Group (CCIG), Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal.
Good practices in artificial intelligence (AI) model validation are key for achieving trustworthy AI. Within the cancer imaging domain, attracting the attention of clinical and technical AI enthusiasts, this work discusses current gaps in AI validation strategies, examining existing practices that are common or variable across technical groups (TGs) and clinical groups (CGs). The work is based on a set of structured questions encompassing several AI validation topics, addressed to professionals working in AI for medical imaging.
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