Risk Management

Novel Machine Learning HIV Intervention for Sexual and Gender Minority Young People Who Have Sex With Men (uTECH): Protocol for a Randomized Comparison Trial.

BACKGROUND: Sexual and gender minority (SGM) young people are disproportionately affected by HIV in the United States, and substance use is a major driver of new infections. People who use web-based venues to meet sex partners are more likely to report substance use, sexual risk behaviors, and sexually transmitted infections. To our knowledge, no machine learning (ML) interventions have been devel...

Automatic deep learning segmentation of the hippocampus on high-resolution diffusion magnetic resonance imaging and its application to the healthy lifespan.

Diffusion tensor imaging (DTI) can provide unique contrast and insight into microstructural changes with age or disease of the hippocampus, although it is difficult to measure the hippocampus because of its comparatively small size, location, and shape. This has been markedly improved by the advent of a clinically feasible 1-mm isotropic resolution 6-min DTI protocol at 3 T of the hippocampus with...

Ameliorating Racial Disparities in HIV Prevention via a Nurse-Led, AI-Enhanced Program for Pre-Exposure Prophylaxis Utilization Among Black Cisgender Women: Protocol for a Mixed Methods Study.

BACKGROUND: HIV pre-exposure prophylaxis (PrEP) is a critical biomedical strategy to prevent HIV transmission among cisgender women. Despite its prove...

Development of Automated Triggers in Ambulatory Settings in Brazil: Protocol for a Machine Learning-Based Design Thinking Study.

BACKGROUND: The use of technologies has had a significant impact on patient safety and the quality of care and has increased globally. In the literatu...

A comprehensive overview of barriers and strategies for AI implementation in healthcare: Mixed-method design.

Implementation of artificial intelligence systems for healthcare is challenging. Understanding the barriers and implementation strategies can impact t...

Identification of Psychological Treatment Dropout Predictors Using Machine Learning Models on Italian Patients Living with Overweight and Obesity Ineligible for Bariatric Surgery.

According to the main international guidelines, patients with obesity and psychiatric/psychological disorders who cannot be addressed to surgery are r...

Adversarial EM for variational deep learning: Application to semi-supervised image quality enhancement in low-dose PET and low-dose CT.

In positron emission tomography (PET) and X-ray computed tomography (CT), reducing radiation dose can cause significant degradation in image quality. ...

Protocol for machine-learning-based 3D image analysis of nuclear envelope tubules in cultured cells.

The nuclear envelope can form complex structures in physiological and pathological contexts. Current approaches to quantify nuclear envelope structure...

The real ethical issues with AI for clinical psychiatry.

This article explores the ethical issues arising from ordinary AI applications currently used in mental health care, rather than speculative future sc...

An abbreviated Chinese dyslexia screening behavior checklist for primary school students using a machine learning approach.

To increase early identification and intervention of dyslexia, a prescreening instrument is critical to identifying children at risk. The present work...

A machine learning approach to predict in vivo skin growth.

Since their invention, tissue expanders, which are designed to trigger additional skin growth, have revolutionised many reconstructive surgeries. Curr...

Optimal synchronization with L-gain performance: An adaptive dynamic programming approach.

This paper studies an optimal synchronous control protocol design for nonlinear multi-agent systems under partially known dynamics and uncertain exter...

Protocol-based control for semi-Markov reaction-diffusion neural networks.

This paper addresses the asynchronous control problem for semi-Markov reaction-diffusion neural networks (SMRDNNs) under probabilistic event-triggered...

Artificial intelligence in total and unicompartmental knee arthroplasty.

The application of Artificial intelligence (AI) and machine learning (ML) tools in total (TKA) and unicompartmental knee arthroplasty (UKA) emerges wi...

A Systematic Review of Artificial Intelligence Models for Time-to-Event Outcome Applied in Cardiovascular Disease Risk Prediction.

Artificial intelligence (AI) based predictive models for early detection of cardiovascular disease (CVD) risk are increasingly being utilised. However...

Ethical Implications of Artificial Intelligence in Gastroenterology: The Co-pilot or the Captain?

Though artificial intelligence (AI) is being widely implemented in gastroenterology (GI) and hepatology and has the potential to be paradigm shifting ...

AI as a Medical Device Adverse Event Reporting in Regulatory Databases: Protocol for a Systematic Review.

BACKGROUND: The reporting of adverse events (AEs) relating to medical devices is a long-standing area of concern, with suboptimal reporting due to a r...

Emergence of Artificial Intelligence and Machine Learning Models in Sudden Cardiac Arrest: A Comprehensive Review of Predictive Performance and Clinical Decision Support.

Sudden cardiac death/sudden cardiac arrest (SCD/SCA) is an increasingly prevalent cause of mortality globally, particularly in individuals with preexi...

Continual pre-training mitigates forgetting in language and vision.

Pre-trained models are commonly used in Continual Learning to initialize the model before training on the stream of non-stationary data. However, pre-...

Detecting Algorithmic Errors and Patient Harms for AI-Enabled Medical Devices in Randomized Controlled Trials: Protocol for a Systematic Review.

BACKGROUND: Artificial intelligence (AI) medical devices have the potential to transform existing clinical workflows and ultimately improve patient ou...