In the current development and deployment of many artificial intelligence (AI) systems in healthcare, algorithm fairness is a challenging problem in delivering equitable care. Recent …
Artificial intelligence (AI) has demonstrated the ability to extract insights from data, but the fairness of such data-driven insights remains a concern in high-stakes fields. Despite …
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 …
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 …
Machine learning models are increasingly adopted for facilitating clinical decision-making. However, recent research has shown that machine learning techniques may result in …
The significant advancements in applying artificial intelligence (AI) to healthcare decision- making, medical diagnosis, and other domains have simultaneously raised concerns about …
I Straw - Artificial intelligence in medicine, 2020 - Elsevier
Medicine is at a disciplinary crossroads. With the rapid integration of Artificial Intelligence (AI) into the healthcare field the future care of our patients will depend on the decisions we …
In this review, we address the issue of fairness in the clinical integration of artificial intelligence (AI) in the medical field. As the clinical adoption of deep learning algorithms, a …
JK Paulus, DM Kent - NPJ digital medicine, 2020 - nature.com
The machine learning community has become alert to the ways that predictive algorithms can inadvertently introduce unfairness in decision-making. Herein, we discuss how concepts …