Purpose Of Review: The goal of automation is to decrease the anesthesiologist's workload and to decrease the possibility of human error. Automated systems introduce problems of its own, however, including loss of situation awareness, leaving the physician out of the loop, and training physicians how to monitor autonomous systems. This review will discuss the growing role of automated systems in healthcare and describe two types of automation failures.
Recent Findings: An automation surprise occurs when an automated system takes an action that is unexpected by the user. Mode confusion occurs when the operator does not understand what an automated system is programmed to do and may prevent the clinician from fully understanding what the device is doing during a critical event. Both types of automation failures can decrease a clinician's trust in the system. They may also prevent a clinician from regaining control of a failed system (e.g., a ventilator that is no longer working) during a critical event.
Summary: Clinicians should receive generalized training on how to manage automation and should also be required to demonstrate competency before using medical equipment that employs automation, including electronic health records, infusion pumps, and ventilators.
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http://dx.doi.org/10.1097/ACO.0000000000000935 | DOI Listing |
ISA Trans
January 2025
School of Artificial Intelligence, Anhui University, Hefei 230601, China. Electronic address:
This study investigates pigeon-like flexible flapping wings, which are known for their low energy consumption, high flexibility, and lightweight design. However, such flexible flapping wing systems are prone to deformation and vibration during flight, leading to performance degradation. It is thus necessary to design a control method to effectively manage the vibration of flexible wings.
View Article and Find Full Text PDFComput Biol Med
January 2025
Division of Electronics and Information Engineering, College of Engineering, Jeonbuk National University, 567, Baekje-daero, Deokjin-gu, 54896, Jeonju, Republic of Korea. Electronic address:
Kidney stone is a common urological disease in dogs and can lead to serious complications such as pyelonephritis and kidney failure. However, manual diagnosis involves a lot of burdens on radiologists and may cause human errors due to fatigue. Automated methods using deep learning models have been explored to overcome this limitation.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Neurobehavioral Systems, Inc, Berkeley, CA, USA.
Background: Semantic memory assessments are sensitive indicators of cognitive decline in pre-clinical Alzheimer's Disease (AD), with slowed reaction time and diminished accuracy serving as markers for amyloid accumulation. We introduce the Semantic Stroop Test-a brief (4.3 minute), automated semantic retrieval and executive function task included in the California Cognitive Assessment Battery (CCAB).
View Article and Find Full Text PDFEur Heart J Case Rep
January 2025
Cardiology Department, Meir Medical Center, Tchernichovsky St 59, Kfar Saba 4418001, Israel.
Background: Anomalous origin of the left coronary artery (LCA) from the pulmonary artery (PA) (ALCAPA) is a rare congenital abnormality. We present a case of an ALCAPA in a 25-year-old man.
Case Summary: A 25-year-old male with no past medical history was admitted to our intensive cardiac care unit after sudden cardiac arrest due to ventricular fibrillation and suspected acute coronary syndrome.
Sci Rep
January 2025
Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara, 630-0192, Japan.
Deep learning-based image segmentation has allowed for the fully automated, accurate, and rapid analysis of musculoskeletal (MSK) structures from medical images. However, current approaches were either applied only to 2D cross-sectional images, addressed few structures, or were validated on small datasets, which limit the application in large-scale databases. This study aimed to validate an improved deep learning model for volumetric MSK segmentation of the hip and thigh with uncertainty estimation from clinical computed tomography (CT) images.
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