While interest in the application of machine learning to improve healthcare has grown
tremendously in recent years, a number of barriers prevent deployment in medical practice …

The problem of algorithmic bias represents an ethical threat to the fair treatment of patients
when their care involves machine learning (ML) models informing clinical decision-making …

Abstract Machine learning and artificial intelligence are increasingly deployed in critical
societal functions such as finance, media and healthcare. Along with their deployment come …

Machine learning models are increasingly adopted for facilitating clinical decision-making.
However, recent research has shown that machine learning techniques may result in …

Objectives Fairness is a core concept meant to grapple with different forms of discrimination
and bias that emerge with advances in Artificial Intelligence (eg, machine learning, ML). Yet …

Benefiting from the digitization of healthcare data and the development of computing power,
machine learning methods are increasingly used in the healthcare domain. Fairness …

Abstract Machine learning based systems are reaching society at large and in many aspects
of everyday life. This phenomenon has been accompanied by concerns about the ethical …

Can medical algorithms be fair? Three ethical quandaries and one dilemma - PMC Back to Top
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Objective The integration of artificial intelligence (AI) and machine learning (ML) in health
care to aid clinical decisions is widespread. However, as AI and ML take important roles in …

Background Methods of machine learning provide opportunities to use real-world data to
solve complex problems. Applications of these methods in laboratory medicine promise to …