Patient safety and quality improvement: Ethical principles for a regulatory approach to bias in healthcare machine learning
MD McCradden, S Joshi, JA Anderson… - Journal of the …, 2020 - academic.oup.com
Accumulating evidence demonstrates the impact of bias that reflects social inequality on the
performance of machine learning (ML) models in health care. Given their intended …
performance of machine learning (ML) models in health care. Given their intended …
What's fair is… fair? Presenting JustEFAB, an ethical framework for operationalizing medical ethics and social justice in the integration of clinical machine learning …
M Mccradden, O Odusi, S Joshi, I Akrout… - Proceedings of the …, 2023 - dl.acm.org
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 …
when their care involves machine learning (ML) models informing clinical decision-making …
An empirical characterization of fair machine learning for clinical risk prediction
The use of machine learning to guide clinical decision making has the potential to worsen
existing health disparities. Several recent works frame the problem as that of algorithmic …
existing health disparities. Several recent works frame the problem as that of algorithmic …
Ethical machine learning in healthcare
The use of machine learning (ML) in healthcare raises numerous ethical concerns,
especially as models can amplify existing health inequities. Here, we outline ethical …
especially as models can amplify existing health inequities. Here, we outline ethical …
Ensuring fairness in machine learning to advance health equity
Machine learning is used increasingly in clinical care to improve diagnosis, treatment
selection, and health system efficiency. Because machine-learning models learn from …
selection, and health system efficiency. Because machine-learning models learn from …
Algorithmic fairness in computational medicine
Machine learning models are increasingly adopted for facilitating clinical decision-making.
However, recent research has shown that machine learning techniques may result in …
However, recent research has shown that machine learning techniques may result in …
Algorithmic fairness in artificial intelligence for medicine and healthcare
In healthcare, the development and deployment of insufficiently fair systems of artificial
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …
A research ethics framework for the clinical translation of healthcare machine learning
MD McCradden, JA Anderson… - The American Journal …, 2022 - Taylor & Francis
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 …
healthcare have immense potential to improve the care of patients. While there are some …
Considerations for addressing bias in artificial intelligence for health equity
MD Abràmoff, ME Tarver, N Loyo-Berrios… - NPJ digital …, 2023 - nature.com
Health equity is a primary goal of healthcare stakeholders: patients and their advocacy
groups, clinicians, other providers and their professional societies, bioethicists, payors and …
groups, clinicians, other providers and their professional societies, bioethicists, payors and …
The Potential For Bias In Machine Learning And Opportunities For Health Insurers To Address It: Article examines the potential for bias in machine learning and …
As the use of machine learning algorithms in health care continues to expand, there are
growing concerns about equity, fairness, and bias in the ways in which machine learning …
growing concerns about equity, fairness, and bias in the ways in which machine learning …