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Machine learning for medical imaging: methodological failures and recommendations for the future
Research in computer analysis of medical images bears many promises to improve patients'
health. However, a number of systematic challenges are slowing down the progress of the …
health. However, a number of systematic challenges are slowing down the progress of the …
American Society of Hematology 2021 guidelines on the use of anticoagulation for thromboprophylaxis in patients with COVID-19
Background: Coronavirus disease 2019 (COVID-19)–related critical illness and acute illness
are associated with a risk of venous thromboembolism (VTE). Objective: These evidence …
are associated with a risk of venous thromboembolism (VTE). Objective: These evidence …
Ethical use of artificial intelligence in health professions education: AMEE Guide No. 158
K Masters - Medical Teacher, 2023 - Taylor & Francis
Abstract Health Professions Education (HPE) has benefitted from the advances in Artificial
Intelligence (AI) and is set to benefit more in the future. Just as any technological advance …
Intelligence (AI) and is set to benefit more in the future. Just as any technological advance …
Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on …
Introduction The Transparent Reporting of a multivariable prediction model of Individual
Prognosis Or Diagnosis (TRIPOD) statement and the Prediction model Risk Of Bias …
Prognosis Or Diagnosis (TRIPOD) statement and the Prediction model Risk Of Bias …
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
Abstract Machine learning methods offer great promise for fast and accurate detection and
prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest …
prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest …
Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review
Objective To assess the methodological quality of studies on prediction models developed
using machine learning techniques across all medical specialties. Design Systematic …
using machine learning techniques across all medical specialties. Design Systematic …
[HTML][HTML] Bias in artificial intelligence algorithms and recommendations for mitigation
LH Nazer, R Zatarah, S Waldrip, JXC Ke… - PLOS Digital …, 2023 - journals.plos.org
The adoption of artificial intelligence (AI) algorithms is rapidly increasing in healthcare. Such
algorithms may be shaped by various factors such as social determinants of health that can …
algorithms may be shaped by various factors such as social determinants of health that can …
External validation of prognostic models: what, why, how, when and where?
Prognostic models that aim to improve the prediction of clinical events, individualized
treatment and decision-making are increasingly being developed and published. However …
treatment and decision-making are increasingly being developed and published. However …
Fairness of artificial intelligence in healthcare: review and recommendations
In this review, we address the issue of fairness in the clinical integration of artificial
intelligence (AI) in the medical field. As the clinical adoption of deep learning algorithms, a …
intelligence (AI) in the medical field. As the clinical adoption of deep learning algorithms, a …
Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal
Objective To review and appraise the validity and usefulness of published and preprint
reports of prediction models for prognosis of patients with covid-19, and for detecting people …
reports of prediction models for prognosis of patients with covid-19, and for detecting people …