AI Medical Compendium Topic:
Prospective Studies

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A Comparative Defense of Self-initiated Prospective Moral Answerability for Autonomous Robot harm.

Science and engineering ethics
As artificial intelligence becomes more sophisticated and robots approach autonomous decision-making, debates about how to assign moral responsibility have gained importance, urgency, and sophistication. Answering Stenseke's (2022a) call for scaffold...

From Compressed-Sensing to Deep Learning MR: Comparative Biventricular Cardiac Function Analysis in a Patient Cohort.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Conventional segmented, retrospectively gated cine (Conv-cine) is challenged in patients with breath-hold difficulties. Compressed sensing (CS) has shown values in cine imaging but generally requires long reconstruction time. Recent artif...

Applying a Smartwatch to Predict Work-related Fatigue for Emergency Healthcare Professionals: Machine Learning Method.

The western journal of emergency medicine
INTRODUCTION: Healthcare professionals frequently experience work-related fatigue, which may jeopardize their health and put patient safety at risk. In this study, we applied a machine learning (ML) approach based on data collected from a smartwatch ...

Opportunistic detection of type 2 diabetes using deep learning from frontal chest radiographs.

Nature communications
Deep learning (DL) models can harness electronic health records (EHRs) to predict diseases and extract radiologic findings for diagnosis. With ambulatory chest radiographs (CXRs) frequently ordered, we investigated detecting type 2 diabetes (T2D) by ...

Machine learning to predict environmental dose rates from a radionuclide therapy service - a proof of concept study.

Journal of radiological protection : official journal of the Society for Radiological Protection
The Ionising Radiation Regulations 2017 requires prior risk assessment calculations and regular environmental monitoring of radiation doses. However, the accuracy of prior risk assessments is limited by assumptions and monitoring only provides retros...

Exploring the potential of Chat-GPT as a supportive tool for sialendoscopy clinical decision making and patient information support.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
INTRODUCTION: Sialendoscopy has emerged in the last decades as a groundbreaking technique, offering a minimally invasive approach for exploring and managing salivary gland disorders. More recently, the advent of chatbots, powered by advanced natural ...

A Channel-Dimensional Feature-Reconstructed Deep Learning Model for Predicting Breast Cancer Molecular Subtypes on Overall b-Value Diffusion-Weighted MRI.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Dynamic contrast-enhanced (DCE) MRI commonly outperforms diffusion-weighted (DW) MRI in breast cancer discrimination. However, the side effects of contrast agents limit the use of DCE-MRI, particularly in patients with chronic kidney dise...

Machine learning approaches for data-driven process monitoring of biological wastewater treatment plant: A review of research works on benchmark simulation model No. 1(BSM1).

Environmental monitoring and assessment
In the past decade, machine learning techniques have seen wide industrial applications for design of data-based process monitoring systems with an aim to improve industrial productivity. An efficient process monitoring system for wastewater treatment...

Clinical benefit of AI-assisted lung ultrasound in a resource-limited intensive care unit.

Critical care (London, England)
BACKGROUND: Interpreting point-of-care lung ultrasound (LUS) images from intensive care unit (ICU) patients can be challenging, especially in low- and middle- income countries (LMICs) where there is limited training available. Despite recent advances...

A deep learning method for the automated assessment of paradoxical pulsation after myocardial infarction using multicenter cardiac MRI data.

European radiology
OBJECTIVE: The current study aimed to explore a deep convolutional neural network (DCNN) model that integrates multidimensional CMR data to accurately identify LV paradoxical pulsation after reperfusion by primary percutaneous coronary intervention w...