AIMC Topic: Humans

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Predictive models and applicability of artificial intelligence-based approaches in drug allergy.

Current opinion in allergy and clinical immunology
PURPOSE OF REVIEW: Drug allergy is responsible for a huge burden on public healthcare systems, representing in some instances a threat for patient's life. Diagnosis is complex due to the heterogeneity of clinical phenotypes and mechanisms involved, t...

Heart and great vessels segmentation in congenital heart disease via CNN and conditioned energy function postprocessing.

International journal of computer assisted radiology and surgery
PURPOSE: The segmentation of the heart and great vessels in CT images of congenital heart disease (CHD) is critical for the clinical assessment of cardiac anomalies and the diagnosis of CHD. However, the diverse types and abnormalities inherent in CH...

Machine Learning from Veno-Venous Extracorporeal Membrane Oxygenation Identifies Factors Associated with Neurological Outcomes.

Lung
BACKGROUND: Neurological complications are common in patients receiving veno-venous extracorporeal membrane oxygenation (VV-ECMO) support. We used machine learning (ML) algorithms to identify predictors for neurological outcomes for these patients.

Can Artificial Intelligence Accurately Detect Urinary Stones? A Systematic Review.

Journal of endourology
To perform a systematic review on artificial intelligence (AI) performances to detect urinary stones. A PROSPERO-registered (CRD473152) systematic search of Scopus, Web of Science, Embase, and PubMed databases was performed to identify original res...

Predictive Modeling of Urinary Stone Composition Using Machine Learning and Clinical Data: Implications for Treatment Strategies and Pathophysiological Insights.

Journal of endourology
Preventative strategies and surgical treatments for urolithiasis depend on stone composition. However, stone composition is often unknown until the stone is passed or surgically managed. Given that stone composition likely reflects the physiological...

Neural network dose prediction for cervical brachytherapy: Overcoming data scarcity for applicator-specific models.

Medical physics
BACKGROUND: 3D neural network dose predictions are useful for automating brachytherapy (BT) treatment planning for cervical cancer. Cervical BT can be delivered with numerous applicators, which necessitates developing models that generalize to multip...

Early evaluation of a natural language processing tool to improve access to educational resources for surgical patients.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
PURPOSE: Accessible patient information sources are vital in educating patients about the benefits and risks of spinal surgery, which is crucial for obtaining informed consent. We aim to assess the effectiveness of a natural language processing (NLP)...

Teaching clinical reasoning: principles from the literature to help improve instruction from the classroom to the bedside.

Korean journal of medical education
Clinical reasoning has been characterized as being an essential aspect of being a physician. Despite this, clinical reasoning has a variety of definitions and medical error, which is often attributed to clinical reasoning, has been reported to be a l...

A Quadruple Revolution: Deciphering Biological Complexity with Artificial Intelligence, Multiomics, Precision Medicine, and Planetary Health.

Omics : a journal of integrative biology
A quiet quadruple revolution has been in the making in systems science with convergence of (1) artificial intelligence, machine learning, and other digital technologies; (2) multiomics big data integration; (3) growing interest in the "variability sc...