AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

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Building digital patient pathways for the management and treatment of multiple sclerosis.

Frontiers in immunology
Recent advances in the field of artificial intelligence (AI) could yield new insights into the potential causes of multiple sclerosis (MS) and factors influencing its course as the use of AI opens new possibilities regarding the interpretation and us...

Identifying Functional Status Impairment in People Living With Dementia Through Natural Language Processing of Clinical Documents: Cross-Sectional Study.

Journal of medical Internet research
BACKGROUND: Assessment of activities of daily living (ADLs) and instrumental ADLs (iADLs) is key to determining the severity of dementia and care needs among older adults. However, such information is often only documented in free-text clinical notes...

Deep learning-based correction of cataract-induced influence on macular pigment optical density measurement by autofluorescence spectroscopy.

PloS one
PURPOSE: Measurements of macular pigment optical density (MPOD) using the autofluorescence spectroscopy yield underestimations of actual values in eyes with cataracts. Previously, we proposed a correction method for this error using deep learning (DL...

Study of Serum Fibroblast Growth Factor 23 as a Predictor of Endothelial Dysfunction among Egyptian Patients with Diabetic Kidney Disease.

Saudi journal of kidney diseases and transplantation : an official publication of the Saudi Center for Organ Transplantation, Saudi Arabia
Endothelial dysfunction in patients with diabetic nephropathy is caused by nontraditional factors in addition to common risk factors (e.g., hypertension) in people with normal kidney function. These nontraditional factors include factors involved in ...

Prediction of xerostomia in elderly based on clinical characteristics and salivary flow rate with machine learning.

Scientific reports
Xerostomia may be accompanied by changes in salivary flow rate and the incidence increases in elderly. We aimed to use machine learning algorithms, to identify significant predictors for the presence of xerostomia. This study is the first to predict ...

A comparative study of explainable ensemble learning and logistic regression for predicting in-hospital mortality in the emergency department.

Scientific reports
This study addresses the challenges associated with emergency department (ED) overcrowding and emphasizes the need for efficient risk stratification tools to identify high-risk patients for early intervention. While several scoring systems, often bas...

Assessing Fuchs Corneal Endothelial Dystrophy Using Artificial Intelligence-Derived Morphometric Parameters From Specular Microscopy Images.

Cornea
PURPOSE: The aim of this study was to evaluate the efficacy of artificial intelligence-derived morphometric parameters in characterizing Fuchs corneal endothelial dystrophy (FECD) from specular microscopy images.

Perceptions and Knowledge of Undergraduate Dental Students about Artificial Intelligence in Dental Schools: A Cross-sectional Study.

The journal of contemporary dental practice
OBJECTIVE: This study aims to assess the perceptions and knowledge of undergraduate dental students about artificial intelligence (AI) in dental schools through a cross-sectional study.

Perceptions on artificial intelligence-based decision-making for coexisting multiple long-term health conditions: protocol for a qualitative study with patients and healthcare professionals.

BMJ open
INTRODUCTION: Coexisting multiple health conditions is common among older people, a population that is increasing globally. The potential for polypharmacy, adverse events, drug interactions and development of additional health conditions complicates ...