A5063981686- Artificial Intelligence in Healthcare and Education
- Ethics in Clinical Research
- Explainable Artificial Intelligence (XAI)
- Machine Learning in Healthcare
- Healthcare cost, quality, practices
- Health Systems, Economic Evaluations, Quality of Life
- Meta-analysis and systematic reviews
- Radiomics and Machine Learning in Medical Imaging
- Ethics and Social Impacts of AI
- Artificial Intelligence in Healthcare
- Biomedical Ethics and Regulation
- Child Nutrition and Feeding Issues
- Traumatic Brain Injury Research
- Ethics in medical practice
- Ethics and Legal Issues in Pediatric Healthcare
- Child and Adolescent Health
- Autopsy Techniques and Outcomes
- Neurogenetic and Muscular Disorders Research
- COVID-19 and healthcare impacts
- Family and Disability Support Research
- Mobile Health and mHealth Applications
- Medical Imaging and Analysis
- Sports injuries and prevention
- Childhood Cancer Survivors' Quality of Life
- AI in cancer detection
Australian Centre for Robotic Vision
2024-2025
Hospital for Sick Children
2019-2025
SickKids Foundation
2019-2025
The University of Adelaide
2024-2025
Women's and Children's Health Network
2024-2025
Public Health Ontario
2018-2024
University of Toronto
2018-2024
Women's and Children's Hospital
2024
3M (United States)
2020-2024
Artificial Intelligence in Medicine (Canada)
2024
The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) statement was published in 2015 to provide the minimum reporting recommendations studies developing or evaluating performance model. Methodological advances field have since included widespread use artificial intelligence (AI) powered by machine learning methods develop models. An update is thus needed. TRIPOD+AI provides harmonised guidance studies, irrespective whether regression...
Artificial intelligence has exposed pernicious bias within health data that constitutes substantial ethical threat to the use of machine learning in medicine.1Char DS Shah NH Magnus D Implementing care—addressing challenges.N Engl J Med. 2018; 378: 981-983Crossref PubMed Scopus (445) Google Scholar, 2Obermeyer Z Powers B Vogeli C Mullainathan S Dissecting racial an algorithm used manage populations.Science. 2019; 366: 447-453Crossref (1151) Scholar Solutions algorithmic fairness have been...
A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, silico, evaluation, but few have yet demonstrated real benefit to patient care. Early stage evaluation is important assess an AI system’s actual at small scale, ensure its safety, evaluate the human factors surrounding use, and pave way further large scale trials. However, reporting these early studies remains inadequate. The present statement provides a...
Translating machine learning (ML) models effectively to clinical practice requires establishing clinicians' trust. Explainability, or the ability of an ML model justify its outcomes and assist clinicians in rationalizing prediction, has been generally understood be critical However, field suffers from lack concrete definitions for usable explanations different settings. To identify specific aspects explainability that may catalyze building trust models, we surveyed two distinct acute care...
Abstract Artificial intelligence as a medical device is increasingly being applied to healthcare for diagnosis, risk stratification and resource allocation. However, growing body of evidence has highlighted the algorithmic bias, which may perpetuate existing health inequity. This problem arises in part because systemic inequalities dataset curation, unequal opportunity participate research access. study aims explore standards, frameworks best practices ensuring adequate data diversity...
The application of artificial intelligence and machine learning (ML) technologies in healthcare have immense potential to improve the care patients. While there are some emerging practices surrounding responsible ML as well regulatory frameworks, traditional role research ethics oversight has been relatively unexplored regarding its relevance for clinical ML. In this paper, we provide a comprehensive framework that can apply systematic inquiry across development cycle. pathway consists three...
<h3>Background:</h3> As artificial intelligence (AI) approaches in research increase and AI becomes more integrated into medicine, there is a need to understand perspectives from members of the Canadian public medical community. The aim this project was investigate current on ethical issues surrounding health care. <h3>Methods:</h3> In qualitative study, adult patients with meningioma their caregivers were recruited consecutively (August 2018–February 2019) neurosurgical clinic Toronto....
Accumulating evidence demonstrates the impact of bias that reflects social inequality on performance machine learning (ML) models in health care. Given their intended placement within healthcare decision making more broadly, ML tools require attention to adequately quantify and reduce its potential exacerbate inequalities. We suggest taking a patient safety quality improvement approach can support quantification bias-related effects ML. Drawing from ethical principles underpinning these...
Researchers are studying how artificial intelligence (AI) can be used to better detect, prognosticate and subgroup diseases. The idea that AI might advance medicine’s understanding of biological categories psychiatric disorders, as well provide treatments, is appealing given the historical challenges with prediction, diagnosis treatment in psychiatry. Given power analyse vast amounts information, some clinicians may feel obligated align their clinical judgements outputs system. However, a...
Importance Understanding the views and values of patients is substantial importance to developing ethical parameters artificial intelligence (AI) use in medicine. Thus far, there limited study on children youths. Their perspectives contribute meaningfully integration AI Objective To explore moral attitudes youths regarding research clinical care involving health at point care. Design, Setting, Participants This qualitative recruited participants younger than 18 years during a 1-year period...
The Prediction model Risk Of Bias ASsessment Tool (PROBAST) is used to assess the quality, risk of bias, and applicability prediction models or algorithms model/algorithm studies. Since PROBAST's introduction in 2019, much progress has been made methodology for modelling use artificial intelligence, including machine learning, techniques. An update PROBAST-2019 thus needed. This article describes development PROBAST+AI. PROBAST+AI consists two distinctive parts: evaluation. For development,...