BACKGROUND AND PURPOSE: In comparison to conventional medical imaging diagnostic modalities, the aim of this overview article is to analyze the accuracy of the application of Artificial Intelligence (AI) techniques in the identification and diagnosis...
BACKGROUND: There is considerable geographic heterogeneity in obesity prevalence across counties in the United States. Machine learning algorithms accurately predict geographic variation in obesity prevalence, but the models are often uninterpretable...
Persons with a history of traumatic brain injury (TBI) may exhibit short- and long-term cognitive deficits as well as psychiatric symptoms. These symptoms often reflect functional anomalies in the brain that are not detected by standard neuroimaging...
Colon cancer is one of the malignant tumors with high morbidity, lethality, and prevalence across global human health. Molecular biomarkers play key roles in its prognosis. In particular, immune-related lncRNAs (IRL) have attracted enormous interest ...
Rheumatic disorders present a global health challenge, marked by inflammation and damage to joints, bones, and connective tissues. Accurate, timely diagnosis and appropriate management are crucial for favorable patient outcomes. Magnetic resonance im...
In domains such as medical and healthcare, the interpretability and explainability of machine learning and artificial intelligence systems are crucial for building trust in their results. Errors caused by these systems, such as incorrect diagnoses or...
We propose the "runtime learning" hypothesis which states that people quickly learn to perform unfamiliar tasks as the tasks arise by using task-relevant instances of concepts stored in memory during mental training. To make learning rapid, the hypot...
OBJECTIVE: Our study aimed to determine whether radiomics models based on contrast-enhanced computed tomography (CECT) have considerable ability to predict serosal involvement in gallbladder cancer (GBC) patients.
PURPOSE OF REVIEW: Recent technological advances have accelerated the use of Machine Learning in trauma science. This review provides an overview on the available evidence for research and patient care. The review aims to familiarize clinicians with ...
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