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Developing, implementing and governing artificial intelligence in medicine: a step-by-step approach to prevent an artificial intelligence winter
Conceptualising fairness: three pillars for medical algorithms and health equity
Investigating for bias in healthcare algorithms: a sex-stratified analysis of supervised machine learning models in liver disease prediction
Machine learning in medical education: a survey of the experiences and opinions of medical students in Ireland
Eight human factors and ergonomics principles for healthcare artificial intelligence
Data consistency in the English Hospital Episodes Statistics database
Can medical algorithms be fair? Three ethical quandaries and one dilemma
Evaluating algorithmic fairness in the presence of clinical guidelines: the case of atherosclerotic cardiovascular disease risk estimation
Early detection of autism spectrum disorder in young children with machine learning using medical claims data
Connecting artificial intelligence and primary care challenges: findings from a multi stakeholder collaborative consultation
Assuring the safety of AI-based clinical decision support systems: a case study of the AI Clinician for sepsis treatment
Towards a harmonised framework for developing quality of care indicators for global health: a scoping review of existing conceptual and methodological practices
A proposal for developing a platform that evaluates algorithmic equity and accuracy
Resampling to address inequities in predictive modeling of suicide deaths
User-centred design for machine learning in health care: a case study from care management
Improving smart medication management': an online expert discussion
Operationalising fairness in medical AI adoption: detection of early Alzheimer's disease with 2D CNN
Predicting emerging SARS-CoV-2 variants of concern through a One Class dynamic anomaly detection algorithm
Global disparity bias in ophthalmology artificial intelligence applications
Establishing a colorectal cancer research database from routinely collected health data: the process and potential from a pilot study