AI Medical Compendium Topic:
Child

Clear Filters Showing 811 to 820 of 3010 articles

Transmission Versus Truth, Imitation Versus Innovation: What Children Can Do That Large Language and Language-and-Vision Models Cannot (Yet).

Perspectives on psychological science : a journal of the Association for Psychological Science
Much discussion about large language models and language-and-vision models has focused on whether these models are intelligent agents. We present an alternative perspective. First, we argue that these artificial intelligence (AI) models are cultural ...

Machine learning in medication prescription: A systematic review.

International journal of medical informatics
BACKGROUND: Medication prescription is a complex process that could benefit from current research and development in machine learning through decision support systems. Particularly pediatricians are forced to prescribe medications "off-label" as chil...

Accelerated Cine Cardiac MRI Using Deep Learning-Based Reconstruction: A Systematic Evaluation.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Breath-holding (BH) for cine balanced steady state free precession (bSSFP) imaging is challenging for patients with impaired BH capacity. Deep learning-based reconstruction (DLR) of undersampled k-space promises to shorten BHs while prese...

Deep learning-based identification of spine growth potential on EOS radiographs.

European radiology
OBJECTIVES: To develop an automatic computer-based method that can help clinicians in assessing spine growth potential based on EOS radiographs.

Comparison of Machine Learning Algorithms Using Manual/Automated Features on 12-Lead Signal Electrocardiogram Classification: A Large Cohort Study on Students Aged Between 6 to 18 Years Old.

Cardiovascular engineering and technology
PROPOSE: An electrocardiogram (ECG) has been extensively used to detect rhythm disturbances. We sought to determine the accuracy of different machine learning in distinguishing abnormal ECGs from normal ones in children who were examined using a rest...

An artificial intelligence study: automatic description of anatomic landmarks on panoramic radiographs in the pediatric population.

BMC oral health
BACKGROUND: Panoramic radiographs, in which anatomic landmarks can be observed, are used to detect cases closely related to pediatric dentistry. The purpose of the study is to investigate the success and reliability of the detection of maxillary and ...

AI's deep dive into complex pediatric inguinal hernia issues: a challenge to traditional guidelines?

Hernia : the journal of hernias and abdominal wall surgery
OBJECTIVE: This study utilized ChatGPT, an artificial intelligence program based on large language models, to explore controversial issues in pediatric inguinal hernia surgery and compare its responses with the guidelines of the European Association ...

Natural language processing for identification of refractory status epilepticus in children.

Epilepsia
OBJECTIVE: Pediatric status epilepticus is one of the most frequent pediatric emergencies, with high mortality and morbidity. Utilizing electronic health records (EHRs) permits analysis of care approaches and disease outcomes at a lower cost than pro...

Detection and classification of adult epilepsy using hybrid deep learning approach.

Scientific reports
The electroencephalogram (EEG) has emerged over the past few decades as one of the key tools used by clinicians to detect seizures and other neurological abnormalities of the human brain. The proper diagnosis of epilepsy is crucial due to its distinc...

Convolutional Neural Network for Fully Automated Cerebellar Volumetry in Children in Comparison to Manual Segmentation and Developmental Trajectory of Cerebellar Volumes.

Cerebellum (London, England)
The purpose of this study was to develop a fully automated and reliable volumetry of the cerebellum of children during infancy and childhood using deep learning algorithms in comparison to manual segmentation. In addition, the clinical usefulness of ...