Primary Care

Autonomous Systems and Artificial Intelligence in Healthcare Transformation to 5P Medicine - Ethical Challenges.

The paper introduces a structured approach to transforming healthcare towards personalized, preventi...

Machine Learning-Based Identification of Obesity from Positive and Unlabelled Electronic Health Records.

INTRODUCTION: Prevalence of overweight and obesity are increas- ing in the last decades, and with th...

Machine Learning Assisted Citation Screening for Systematic Reviews.

Evidence-based practice is highly dependent upon up-to-date systematic reviews (SR) for decision mak...

Automatic Extraction of Risk Factors for Dialysis Patients from Clinical Notes Using Natural Language Processing Techniques.

Studies have shown that mental health and comorbidities such as dementia, diabetes and cardiovascula...

Amasyali and Baldwin, Editorial Comment on: The Impact of Obesity in Patients Undergoing Robotic Partial Nephrectomy by Rosen et al. (J Endourol 2020;33(6):431-437; DOI: 10.1089/end.2019.0018).

Performance of deep neural network-based artificial intelligence method in diabetic retinopathy screening: a systematic review and meta-analysis of diagnostic test accuracy.

OBJECTIVE: Automatic diabetic retinopathy screening system based on neural networks has been used to...

Automated MRI-Based Deep Learning Model for Detection of Alzheimer's Disease Process.

In the context of neuro-pathological disorders, neuroimaging has been widely accepted as a clinical ...

[Digital data for more efficient prevention: ethical and legal considerations regarding potentials and risks].

Digitization offers considerable potential for strengthening prevention in the healthcare system. Da...

Wearables in cardiology: Here to stay.

The adoption of wearables in medicine has rapidly expanded worldwide. New generations of wearables a...

Machine Learning and Deep Neural Network Applications in the Thorax: Pulmonary Embolism, Chronic Thromboembolic Pulmonary Hypertension, Aorta, and Chronic Obstructive Pulmonary Disease.

The radiologic community is rapidly integrating a revolution that has not fully entered daily practi...

Artificial Intelligence for Refractive Surgery Screening: Finding the Balance Between Myopia and Hype-ropia.

Screening Candidates for Refractive Surgery With Corneal Tomographic-Based Deep Learning.

IMPORTANCE: Evaluating corneal morphologic characteristics with corneal tomographic scans before ref...

Patient Perspectives on the Use of Artificial Intelligence for Skin Cancer Screening: A Qualitative Study.

IMPORTANCE: The use of artificial intelligence (AI) is expanding throughout the field of medicine. I...

[Medical imaging professionals and related specialties : a questioning is essential!].

Nowadays, we are facing an overwhelming amount of public announcements concerning the rise of artifi...

Radiomics and Deep Learning: Hepatic Applications.

Radiomics and deep learning have recently gained attention in the imaging assessment of various live...

Depression screening using mobile phone usage metadata: a machine learning approach.

OBJECTIVE: Depression is currently the second most significant contributor to non-fatal disease burd...

Machine learning reveals serum sphingolipids as cholesterol-independent biomarkers of coronary artery disease.

BACKGROUNDCeramides are sphingolipids that play causative roles in diabetes and heart disease, with ...

Wearable health devices and personal area networks: can they improve outcomes in haemodialysis patients?

Digitization of healthcare will be a major innovation driver in the coming decade. Also, enabled by ...

Glaucoma screening: where are we and where do we need to go?

PURPOSE OF REVIEW: Current recommendations for glaucoma screening are decidedly neutral. No studies ...

Artificial Intelligence Improves Breast Cancer Screening in Study.

Commentary: Pediatric eye screening - Reaching the unreached.