Standfirst: AI-based models may amplify pre-existing human bias within datasets; addressing this problem will require fundamental a realignment of the culture of software development.
OBJECTIVES: Different stakeholders may hold varying attitudes towards artificial intelligence (AI) applications in healthcare, which may constrain their acceptance if AI developers fail to take them into account. We set out to ascertain evidence of t...
OBJECTIVE: To assess whether an electrocardiography-based artificial intelligence (AI) algorithm developed to detect severe ventricular dysfunction (left ventricular ejection fraction [LVEF] of 35% or below) independently predicts long-term mortality...
Profound advances in computational methods, including artificial intelligence (AI), present the opportunity to use the exponentially growing volume and complexity of available cancer measurements toward data-driven personalized care. While exciting, ...
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