AIMC Topic: Canada

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Machine learning models using non-invasive tests & B-mode ultrasound to predict liver-related outcomes in metabolic dysfunction-associated steatotic liver disease.

Scientific reports
Advanced metabolic-dysfunction-associated steatotic liver disease (MASLD) fibrosis (F3-4) predicts liver-related outcomes. Serum and elastography-based non-invasive tests (NIT) cannot yet reliably predict MASLD outcomes. The role of B-mode ultrasound...

Spondyloarthritis Research and Treatment Network (SPARTAN) Clinical and Imaging Year in Review 2024.

Current rheumatology reports
PURPOSE OF REVIEW: Diagnostic delay remains a critical challenge in axial spondyloarthritis (axSpA). This review highlights key clinical and imaging research from 2024 that addresses this persistent issue, with a focus on the evolving roles of MRI, a...

Machine learning and the labor market: A portrait of occupational and worker inequities in Canada.

Social science & medicine (1982)
INTRODUCTION: Machine learning (ML), an artificial intelligence (AI) subfield, is increasingly used by Canadian workplaces. Concerningly, the impact of ML may be inequitable and contribute to social and health inequities in the working population. Th...

It's raining bots: how easier access to internet surveys has created the perfect storm.

BMJ open quality
Online surveys are an increasingly common way to collect data from the public, with social media and financial incentives (e.g. gift cards) commonly used to increase participation rates. Anonymity, ease of response, and the potential to reach diverse...

Use of Virtual Reality in the Pediatric Perioperative Setting and for Induction of Anesthesia: Mixed Methods Pilot Feasibility Study.

JMIR perioperative medicine
BACKGROUND: Children commonly experience high levels of anxiety prior to surgery. This distress is associated with postoperative maladaptive behaviors. Virtual reality (VR) is an innovative tool for reducing anxiety and pain during various medical pr...

Machine Learning Models Can Predict Tinnitus and Noise-Induced Hearing Loss.

Ear and hearing
OBJECTIVES: Despite the extensive use of machine learning (ML) models in health sciences for outcome prediction and condition classification, their application in differentiating various types of auditory disorders remains limited. This study aimed t...

The Impact of Telepresence Robots on Family Caregivers and Residents in Long-Term Care.

International journal of environmental research and public health
Telepresence robots can enhance social connection and support person-centered care in long-term care (LTC) homes. This study evaluates their impact in facilitating virtual visits between family caregivers and older residents in Canadian LTC homes. Te...

AIFM-ed Curriculum Framework for Postgraduate Family Medicine Education on Artificial Intelligence: Mixed Methods Study.

JMIR medical education
BACKGROUND: As health care moves to a more digital environment, there is a growing need to train future family doctors on the clinical uses of artificial intelligence (AI). However, family medicine training in AI has often been inconsistent or lackin...

Population-level individualized prospective prediction of opioid overdose using machine learning.

Molecular psychiatry
The opioid overdose epidemic has rapidly expanded in North America, with rates accelerating during the COVID-19 pandemic. No existing study has demonstrated prospective opioid overdose at a population level. This study aimed to develop and validate a...

Opinions and Perspectives of Canadian Occupational Therapists on Artificial Intelligence.

Canadian journal of occupational therapy. Revue canadienne d'ergotherapie
Technology is rapidly being developed to improve healthcare outcomes. However, the attitudes and perceptions of occupational therapists (OTs) on artificial intelligence (AI) in healthcare are not yet known. This study aims to: explore Canadian OTs'...