AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
A clear gap remains in the standardization of usability and human-computer interaction metrics for evaluating visual diagnostic AI systems that integrate a clinician-facing interface, suggesting a critical direction for future research.
Hospital physicians were more receptive for use of AI technologies in radiology compared to private practitioners, and practice patterns, economic factors, doctor-patient relationships, and frequency of interaction with the radiology department likely influence how AI use in radiology is perceived.
Background/Objectives: Multimodal artificial intelligence (AI) is increasingly used in radiology to combine medical images with radiology reports, clinical narratives, structured health records, laboratory measurements, and other patient data. These systems may support more context-aware interpretation, reporting, and...
Biomedical visualization increasingly spans traditional medical illustration, synthetic medical imaging, vision-language systems, and patient-specific 3D reconstruction. This narrative review examines applications of artificial intelligence (AI) across this field, with particular attention to medical education and scie...
INTRODUCTION AND AIMS The aim of this review was to evaluate applications, methodological characteristics, limitations and future directions of no-code, low-code and automated machine learning (LCNC/AutoML) approaches in healthcare imaging. METHODS PubMed, Web of Science, and Google Scholar were searched using terms...
PURPOSE Visual analytics is a nascent and underexplored area in the medical education literature that can assist in synthesizing large volumes of data and information to provide actionable insight. This scoping review aims to identify: 1) the characteristics of the studies that have applied visual analytics in medical...
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