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http://dx.doi.org/10.1002/ajmg.a.63764 | DOI Listing |
Light Sci Appl
September 2025
State Key Laboratory of Flexible Electronics, Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NUPT), Nanjing, 210023, China.
As the demand for edge platforms in artificial intelligence increases, including mobile devices and security applications, the surge in data influx into edge devices often triggers interference and suboptimal decision-making. There is a pressing need for solutions emphasizing low power consumption and cost-effectiveness. In-sensor computing systems employing memristors face challenges in optimizing energy efficiency and streamlining manufacturing due to the necessity for multiple physical processing components.
View Article and Find Full Text PDFJMIR Res Protoc
September 2025
School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
Background: Electronic health records (EHRs) have been linked to information overload, which can lead to cognitive fatigue, a precursor to burnout. This can cause health care providers to miss critical information and make clinical errors, leading to delays in care delivery. This challenge is particularly pronounced in medical intensive care units (ICUs), where patients are critically ill and their EHRs contain extensive and complex data.
View Article and Find Full Text PDFRadiology
September 2025
Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck St, Boston, MA 02115.
Despite the rapid growth of Food and Drug Administration-cleared artificial intelligence (AI)- and machine learning-enabled medical devices for use in radiology, current tools remain limited in scope, often focusing on narrow tasks and lacking the ability to comprehensively assist radiologists. These narrow AI solutions face limitations in financial sustainability, operational efficiency, and clinical utility, hindering widespread adoption and constraining their long-term value in radiology practice. Recent advances in generative and multimodal AI have expanded the scope of image interpretation, prompting discussions on the development of generalist medical AI.
View Article and Find Full Text PDFInterv Radiol (Higashimatsuyama)
August 2025
Diagnostic and Interventional Radiology, Nara Medical University, Japan.
Purpose: A newly developed device, the Partially Automated Needle Direction Assistant, was designed to assist with computed tomography-guided oblique needle puncture. This device allows operators to accurately and in real-time determine the puncture angle based on the target lesion and needle entry point. The purpose of this report is to introduce the concept of Partially Automated Needle Direction Assistant and present results from an initial phantom study.
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