Purpose Of Review: Artificial intelligence appears as a potential revolution in the general process of medical training, disease diagnosis and treatment. A novel disruptive technology of the 21st century will be 'learner' robots from artificial intelligence systems able to use all the combination of the available knowledge in medical repositories to give the best standard of care.
Recent Findings: The autonomy level of robots depends on three factors: the complexity of the task; the environment in which the robot operates, and the required level of human-robot interaction. Autonomous robots in healthcare may be classified in delivery, nurse, and surgical robots. The increasing capability of robots to perform independent actions and complex tasks raises responsibility and accountability issues in a wide variety of application domains. Ethical analyses of these issues are underway and are mostly oriented toward the development of ethical policies requiring a law frame on robotic autonomous behaviors.
Summary: Autonomous robots have the potential to improve current medical practice offering a more secure, reliable, and reproducible medicine. Many advancements are required for these new technologies to be fully integrated. Furthermore, the ethical implications of these technologies are yet to be evaluated.
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http://dx.doi.org/10.1097/MOU.0000000000000842 | DOI Listing |
Environ Res
January 2025
Man-Technology-Environment Research Center (MTM), Örebro University, Örebro SE-701 82, Sweden.
As the volume of plastic waste from electrical and electronic equipment (WEEE) continues to rise, a significant portion is disposed of in the environment, with only a small fraction being recycled. Both disposal and recycling pose unknown health risks that require immediate attention. Existing knowledge of WEEE plastic toxicity is limited and mostly relies on epidemiological data and association studies, with few insights into the underlying toxicity mechanisms.
View Article and Find Full Text PDFSensors (Basel)
January 2025
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China.
As advancements in autonomous underwater vehicle (AUV) technology unfold, the role of underwater wireless sensor networks (UWSNs) is becoming increasingly pivotal. However, the high energy consumption in these networks can significantly reduce their operational lifespan, while latency issues can impair overall network performance. To address these challenges, a novel mixed packet forwarding strategy is developed, which incorporates a wakeup threshold and a dynamically adjusted access probability for the cluster head (CH).
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January 2025
The 54th Research Institute, China Electronics Technology Group Corporation, College of Signal and Information Processing, Shijiazhuang 050081, China.
The multi-sensor fusion, such as LiDAR and camera-based 3D object detection, is a key technology in autonomous driving and robotics. However, traditional 3D detection models are limited to recognizing predefined categories and struggle with unknown or novel objects. Given the complexity of real-world environments, research into open-vocabulary 3D object detection is essential.
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January 2025
Department of Mechanical and Intelligent Systems Engineering, The University of Electro-Communications, Tokyo 1828585, Japan.
Recently, aerial manipulations are becoming more and more important for the practical applications of unmanned aerial vehicles (UAV) to choose, transport, and place objects in global space. In this paper, an aerial manipulation system consisting of a UAV, two onboard cameras, and a multi-fingered robotic hand with proximity sensors is developed. To achieve self-contained autonomous navigation to a targeted object, onboard tracking and depth cameras are used to detect the targeted object and to control the UAV to reach the target object, even in a Global Positioning System-denied environment.
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January 2025
School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK.
Ultrasound imaging is widely valued for its safety, non-invasiveness, and real-time capabilities but is often limited by operator variability, affecting image quality and reproducibility. Robot-assisted ultrasound may provide a solution by delivering more consistent, precise, and faster scans, potentially reducing human error and healthcare costs. Effective force control is crucial in robotic ultrasound scanning to ensure consistent image quality and patient safety.
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