Objectives We set out to describe academic machine learning (ML) researchers' ethical
considerations regarding the development of ML tools intended for use in clinical care …

Along with potential benefits to healthcare delivery, machine learning healthcare
applications (ML-HCAs) raise a number of ethical concerns. Ethical evaluations of ML-HCAs …

The application of artificial intelligence and machine learning (ML) technologies in
healthcare have immense potential to improve the care of patients. While there are some …

In their article “A Research Ethics Framework for the Clinical Translation of Healthcare
Machine Learning,” McCradden et al.(2022) highlight the various gaps that emerge when …

The integration of machine learning models into healthcare systems offers unprecedented
opportunities for improving diagnostic accuracy, treatment planning, and patient outcomes …

Increasingly, data scientists are training machinelearning (ML) models for diagnosis,
treatment selection, and resource allocation. The US Food and Drug Administration has …

ML-HCAs have the potential to significantly change an entire healthcare system. It is not
even necessary to presume that this will be disruptive but sufficient to assume that the mere …

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 …

Char et al.(2020) aspire to provide “a systematic approach to identifying… ethical concerns”
around machine learning healthcare applications (ML-HCAs), which includes artificial …

In the past few years, machine learning (ML) tools have been implemented with success in
the medical context. However, several practitioners have raised concerns about the lack of …