In this review, concepts of algorithmic bias and fairness are defined qualitatively and
mathematically. Illustrative examples are given of what can go wrong when unintended bias …

David B. Larson, MD, MBA, FACR, is professor and senior vice chair of strategy and clinical
operations in radiology at Stanford University. He serves as the chair of the Commission on …

Background The development of artificial intelligence (AI) raises ethical concerns about its
side effects on the attitudes and behaviors of clinicians and medical practitioners. The …

Preemptive recognition of the ethical implications of study design and algorithm choices in
artificial intelligence (AI) research is an important but challenging process. AI applications …

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 …

Fueled by new techniques, computational tools, and broader availability of imaging data,
artificial intelligence has the potential to transform the practice of neuroradiology. The recent …

This is a condensed summary of an international multisociety statement on ethics of artificial
intelligence (AI) in radiology produced by the ACR, European Society of Radiology, RSNA …

Artificial intelligence (AI) has an enorgmoust potential to improve radiologic diagnostics,
efficiency, and consistency, through implementation of evidence-based regulations is crucial …

Over the past decade, machine learning (ML) and artificial intelligence (AI) have become
increasingly prevalent in the medical field. In the United States, the Food and Drug …

During the last 2 decades, there has been a shift in the US health care system towards
improving the quality of health care provided by enhancing patient safety and reducing …