Risk Management

A double-blind, crossover, non-inferiority randomised controlled trial where primary care providers and patients compare human-generated and AI-generated digital health messages: the AI-CARE study protocol.

INTRODUCTION: Primary care is facing multiple crises, including an increase in health misinformation. Digital health messaging by primary care providers has been shown to reach a diverse patient population. With the uptake of Generative Artificial Intelligence (GenAI) usage in healthcare, there is an important opportunity to rapidly create messages that are tailored to different populations and co...

The CAMUS Initiative: A Multiphase, Multicentre International Collaboration to Redefine Risk Stratification, Reporting, and Grading of Surgical Complications in Urology.

Surgical complications remain a major source of preventable morbidity, mortality, and health care expenditure, but existing frameworks such as the Clavien-Dindo classification and Comprehensive Complication Index are clinician-centred and intervention-focused and fail to capture cumulative patient-centred outcomes. This protocol outlines the Complications After Major and Minor Urological Surgery (...

Deep learning reconstruction for 40-keV virtual monoenergetic CT of colon cancer: evaluation of image quality and edge sharpness.

BACKGROUND: Virtual monoenergetic imaging (VMI) at 40 keV improves iodine attenuation in colon cancer CT but is constrained by severe image noise. Dee...

Targeted Real-Time Assessment of Chronic Pain (TRAC-Pain) in Youth: Protocol for a Digital Biosignature Development Through a Prospective Observational Cohort Study.

BACKGROUND: Approximately 1 in 5 children and adolescents live with chronic pain, with musculoskeletal (MSK) pain being one of the most prevalent subt...

A machine learning approach to using ultrasound for body composition and nutritional status assessment in newborns: a pilot study protocol.

BACKGROUND: Accurate assessment of infant body composition, specifically fat and fat-free mass, is crucial for evaluating growth and nutritional statu...

[Simulated consciousness, pseudo-empathy and the illusion of attachment: what can psychiatry do with artificial intelligence?].

Over the past decade, the burden of mental disorders has grown while services remain capacity-constrained, pushing generative artificial intelligence ...

From Chatbot to Care Team: A Pilot Study of Generative Artificial Intelligence to Support Nurse-Pharmacist Collaboration and Patient Care in Telemedicine.

Background: Collaboration between nurse practitioners (NPs) and pharmacists is essential for comprehensive patient care, especially in telehealth sett...

Predictive models for ICU patient readmission based on machine learning: A systematic review.

BACKGROUND: Artificial intelligence (AI) prediction models can accurately identify high-risk populations by integrating multi-dimensional clinical dat...

Temporal AI-assisted compressed sensing for high-resolution, motion-robust small-bowel MR enterography without antiperistaltic agents: a feasibility study.

OBJECTIVES: To evaluate whether temporal AI-assisted compressed sensing (tACS) enables high-resolution, motion-robust magnetic resonance enterography ...

The Motion is the Message: Evaluating Motion Tracking Quality for VR Avatars.

Motion tracking to project users into embodied virtual reality (VR) as avatars is an essential application of real-time computer graphics. Most curren...

Use of ambient AI scribe in physicians' clinical documentation: a protocol for a systematic review on effectiveness, efficiency, and satisfaction.

INTRODUCTION: Clinical documentation is a significant driver of burnout among physicians. Ambient artificial intelligence (AI) scribes, which leverage...

Development and Clinical Validation of a Protocol-Agnostic Machine Learning Platform for Automated Treatment Planning in External Beam Radiation Therapy.

PURPOSE: To develop and validate a protocol-agnostic machine learning platform ("Predictive Planning") for knowledge-based planning (KBP) in external ...

From Quantum Mechanics to Coarse-Grained Models: Bridging the Gap toward Polymer Rational Design.

In the rational design of novel polymers, the role of simulation methods based on classical physics is often hindered by the limited accuracy and tran...

Benchmarking large language models for quality control of chest radiographs and CT reports: a retrospective multimodal study.

PURPOSE: This study aims to establish a retrospective, single-centre, feasibility-oriented benchmark for medical imaging quality control(QC) and to ev...

Legal and Ethical Challenges in Integrating AI Into Clinical Practice: Qualitative Study of Physicians' Real-World Experiences.

BACKGROUND: The adoption of artificial intelligence (AI) in health care has accelerated; however, physicians continue to face substantial legal, ethic...

Designing a Carbohydrate Counting App for Young Adults With Type 1 Diabetes: Usability Testing Interview Study.

BACKGROUND: Carbohydrate counting (CC) assists people with type 1 diabetes (T1D) adjust mealtime insulin doses; however, it is often burdensome. Mobil...

Exploring Components and Feasibility of Health Coaching Interventions for Self-Management of Type 2 Diabetes: Protocol for a Systematic Review.

BACKGROUND: Type 2 diabetes (T2D) currently has no cure. However, extensive evidence suggests that addressing key risk factors through lifestyle chang...

Quantitative and qualitative motion analysis for early detection of joint damage in haemophilia patients: study protocol for a pilot feasibility study at a German haemophilia treatment centre.

INTRODUCTION: Joint bleeds are the most significant complication in haemophilia, leading to functional impairment and reduced quality of life. Repeate...

A hybrid spiking convolutional neural framework with extreme learning machine for enhanced anomaly detection in network security.

Real-time cybersecurity systems continue to have a significant problem in detecting breaches in dynamic and highly unbalanced network streams. An onli...

Mobile Imaging-Based Machine Learning for Dental Caries, Sealants, and Fluorosis: Protocol for a Cross-Sectional Model Development and Validation Study.

BACKGROUND: Assessing dental caries, sealants, and fluorosis is essential for public health surveillance, providing critical data to evaluate national...