AIMC Topic: Humans

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Quantifying the automated vehicle safety performance: A scoping review of the literature, evaluation of methods, and directions for future research.

Accident; analysis and prevention
Vehicle automation safety must be evaluated not only for market success but also for more informed decision-making about Automated Vehicles' (AVs) deployment and supporting policies and regulations to govern AVs' unintended consequences. This study i...

Agreement in Risk of Bias Assessment Between RobotReviewer and Human Reviewers: An Evaluation Study on Randomised Controlled Trials in Nursing-Related Cochrane Reviews.

Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing
PURPOSE: RobotReviewer is a machine learning system for semi-automated assistance in risk of bias assessment. The tools's performance in randomized controlled trials (RCTs) in the field of nursing remains unknown. We aimed therefore to evaluate the a...

A multipurpose machine learning approach to predict COVID-19 negative prognosis in São Paulo, Brazil.

Scientific reports
The new coronavirus disease (COVID-19) is a challenge for clinical decision-making and the effective allocation of healthcare resources. An accurate prognostic assessment is necessary to improve survival of patients, especially in developing countrie...

Automatic ganglion cell detection for improving the efficiency and accuracy of hirschprung disease diagnosis.

Scientific reports
Histopathologic diagnosis of Hirschsprung's disease (HSCR) is time consuming and requires expertise. The use of artificial intelligence (AI) in digital pathology is actively researched and may improve the diagnosis of HSCR. The purpose of this resear...

Deep action learning enables robust 3D segmentation of body organs in various CT and MRI images.

Scientific reports
In this study, we propose a novel point cloud based 3D registration and segmentation framework using reinforcement learning. An artificial agent, implemented as a distinct actor based on value networks, is trained to predict the optimal piece-wise li...

HMD-ARG: hierarchical multi-task deep learning for annotating antibiotic resistance genes.

Microbiome
BACKGROUND: The spread of antibiotic resistance has become one of the most urgent threats to global health, which is estimated to cause 700,000 deaths each year globally. Its surrogates, antibiotic resistance genes (ARGs), are highly transmittable be...

Predicting COVID-19 disease progression and patient outcomes based on temporal deep learning.

BMC medical informatics and decision making
BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has caused health concerns worldwide since December 2019. From the beginning of infection, patients will progress through different symptom stages, such as fever, dyspnea or even death. Ide...

Barrett esophagus: What to expect from Artificial Intelligence?

Best practice & research. Clinical gastroenterology
The evaluation and assessment of Barrett's esophagus is challenging for both expert and nonexpert endoscopists. However, the early diagnosis of cancer in Barrett's esophagus is crucial for its prognosis, and could save costs. Pre-clinical and clinica...