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The integration of artificial intelligence (AI) in radiology has brought about substantial advancements and transformative potential in diagnostic imaging practices. This study presents an overview of the current research on the application of AI in radiology, highlighting key insights from recent studies and surveys. These recent studies have explored the expected impact of AI, encompassing machine learning and deep learning, on the work volume of diagnostic radiologists. The present and future role of AI in radiology holds great promise for enhancing diagnostic capabilities, improving workflow efficiency, and ultimately, advancing patient care.
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http://dx.doi.org/10.1016/j.rcl.2024.03.008 | DOI Listing |
J Invest Dermatol
September 2025
Department of Dermatology, CHU Nice, University Côte d'Azur, Nice, France; C3M, INSERM U1065, University Côte d'Azur, Nice, France.
J Am Coll Cardiol
August 2025
Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Cardiology, Kaiser Permanente Santa Clara Medical Center, Santa Clara, California, USA. Electronic address:
Background: Accurate measurement of echocardiographic parameters is crucial for the diagnosis of cardiovascular disease and tracking of change over time; however, manual assessment requires time-consuming effort and can be imprecise. Artificial intelligence has the potential to reduce clinician burden by automating the time-intensive task of comprehensive measurement of echocardiographic parameters.
Objectives: The purpose of this study was to develop and validate open-sourced deep learning semantic segmentation models for the automated measurement of 18 anatomic and Doppler measurements in echocardiography.
J Am Coll Cardiol
August 2025
Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA; Department of Cardiology, Boston Children's Hospital, Boston, Massachusetts, USA.
Anal Chem
September 2025
Department of Applied Chemistry, Faculty of Science and Technology, University of Debrecen, Egyetem tér 1, H-4032 Debrecen, Hungary.
In this Article, we present a novel data analysis method for the determination of copolymer composition from low-resolution mass spectra, such as those recorded in the linear mode of time-of-flight (TOF) mass analyzers. Our approach significantly extends the accessible molecular weight range, enabling reliable copolymer composition analysis even in the higher mass regions. At low resolution, the overlapping mass peaks in the higher mass range hinder a comprehensive characterization of the copolymers.
View Article and Find Full Text PDFMed Teach
September 2025
NordSim, Center for Skills Training and Simulation, Aalborg University Hospital, Aalborg, Denmark.
Background: Assessing skills in simulated settings is resource-intensive and lacks validated metrics. Advances in AI offer the potential for automated competence assessment, addressing these limitations. This study aimed to develop and validate a machine learning AI model for automated evaluation during simulation-based thyroid ultrasound (US) training.
View Article and Find Full Text PDF