AIMC Topic: Artificial Intelligence

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Smart sleep: what to consider when adopting AI-enabled solutions in clinical practice of sleep medicine.

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine
UNLABELLED: Since the publication of its 2020 position statement on artificial intelligence (AI) in sleep medicine by the American Academy of Sleep Medicine, there has been a tremendous expansion of AI-related software and hardware options for sleep ...

Clinical Validation of Artificial Intelligence-Augmented Pathology Diagnosis Demonstrates Significant Gains in Diagnostic Accuracy in Prostate Cancer Detection.

Archives of pathology & laboratory medicine
CONTEXT.—: Prostate cancer diagnosis rests on accurate assessment of tissue by a pathologist. The application of artificial intelligence (AI) to digitized whole slide images (WSIs) can aid pathologists in cancer diagnosis, but robust, diverse evidenc...

Prediction of intradialytic hypotension using pre-dialysis features-a deep learning-based artificial intelligence model.

Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association
BACKGROUND: Intradialytic hypotension (IDH) is a serious complication of hemodialysis (HD) that is associated with increased risks of cardiovascular morbidity and mortality. However, its accurate prediction remains a clinical challenge. The aim of th...

Artificial intelligence suppression as a strategy to mitigate artificial intelligence automation bias.

Journal of the American Medical Informatics Association : JAMIA
BACKGROUND: Incorporating artificial intelligence (AI) into clinics brings the risk of automation bias, which potentially misleads the clinician's decision-making. The purpose of this study was to propose a potential strategy to mitigate automation b...

Efficient prediction of peptide self-assembly through sequential and graphical encoding.

Briefings in bioinformatics
In recent years, there has been an explosion of research on the application of deep learning to the prediction of various peptide properties, due to the significant development and market potential of peptides. Molecular dynamics has enabled the effi...

FG-BERT: a generalized and self-supervised functional group-based molecular representation learning framework for properties prediction.

Briefings in bioinformatics
Artificial intelligence-based molecular property prediction plays a key role in molecular design such as bioactive molecules and functional materials. In this study, we propose a self-supervised pretraining deep learning (DL) framework, called functi...