AIMC Topic: Young Adult
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Large language models can outperform humans in social situational judgments.
Scientific reports
Nov 10, 2024
Large language models (LLM) have been a catalyst for the public interest in artificial intelligence (AI). These technologies perform some knowledge-based tasks better and faster than human beings. However, whether AIs can correctly assess social situ...
Developmental changes in the perceived moral standing of robots.
Cognition
Nov 9, 2024
Emerging evidence suggests that children may think of robots-and artificial intelligence, more generally-as having moral standing. In this paper, we trace the developmental trajectory of this belief. Over three developmental studies (combined N = 415...
Height prediction of individuals with osteogenesis imperfecta by machine learning.
Orphanet journal of rare diseases
Nov 9, 2024
BACKGROUND: Osteogenesis imperfecta (OI) is a genetic disorder characterized by low bone mass, bone fragility and short stature. There is a significant gap in knowledge regarding the growth patterns across different types of OI, and the prediction of...
Predictive modeling of gestational weight gain: a machine learning multiclass classification study.
BMC pregnancy and childbirth
Nov 8, 2024
BACKGROUND: Gestational weight gain (GWG) is a critical factor influencing maternal and fetal health. Excessive or insufficient GWG can lead to various complications, including gestational diabetes, hypertension, cesarean delivery, low birth weight, ...
Joint suppression of cardiac bSSFP cine banding and flow artifacts using twofold phase-cycling and a dual-encoder neural network.
Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
Nov 7, 2024
BACKGROUND: Cardiac balanced steady state free precession (bSSFP) cine imaging suffers from banding and flow artifacts induced by off-resonance. The work aimed to develop a twofold phase cycling sequence with a neural network-based reconstruction (2P...
Academic-related stressors predict depressive symptoms in graduate students: A machine learning study.
Behavioural brain research
Nov 7, 2024
BACKGROUND: Graduate students face higher depression rates worldwide, which were further exacerbated during the COVID-19 pandemic. This study employed a machine learning approach to predict depressive symptoms using academic-related stressors.
Deep learning automatically distinguishes myocarditis patients from normal subjects based on MRI.
The international journal of cardiovascular imaging
Nov 7, 2024
Myocarditis, characterized by inflammation of the myocardial tissue, presents substantial risks to cardiovascular functionality, potentially precipitating critical outcomes including heart failure and arrhythmias. This investigation primarily aims to...
Artificial Intelligence Efficacy as a Function of Trainee Interpreter Proficiency: Lessons from a Randomized Controlled Trial.
AJNR. American journal of neuroradiology
Nov 7, 2024
BACKGROUND AND PURPOSE: Recently, artificial intelligence tools have been deployed with increasing speed in educational and clinical settings. However, the use of artificial intelligence by trainees across different levels of experience has not been ...
Improved patient identification by incorporating symptom severity in deep learning using neuroanatomic images in first episode schizophrenia.
Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology
Nov 6, 2024
Brain alterations associated with illness severity in schizophrenia remain poorly understood. Establishing linkages between imaging biomarkers and symptom expression may enhance mechanistic understanding of acute psychotic illness. Constructing model...
Feature Selection and Machine Learning Approaches in Prediction of Current E-Cigarette Use Among U.S. Adults in 2022.
International journal of environmental research and public health
Nov 6, 2024
Feature selection is essentially the process of picking informative and relevant features from a larger collection of features. Few studies have focused on predictors for current e-cigarette use among U.S. adults using feature selection and machine l...
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