… , regulators, patients, and other stakeholders to ask critical questions and to creatively
imagine novel ways in which generalist models may inadvertently compound health inequities. …

… the risk of exacerbating healthcare inequalities, in particular … pre-existing health inequalities
between different ethnoracial … to check and mitigate algorithmic bias in their tools could be …

… gender and diversity studies, by bringing in expertise on addressing bias and structural
inequalities, provide a crucial source for analyzing and mitigating bias in algorithmic systems. …

… translation of healthcare algorithms built from bad data or racially biased research … bias,
they must be aware of social determinants of health and their impact on healthcare inequalities …

… inequalities in the workplace and in society. This paper reviews, summarises, and synthesises
the current literature related to algorithmic bias … implications of algorithmic bias, whereas …

… potential harms of algorithmic bias, ranging from medico-technical harms (eg, compromising
patient safety) to broader social and public health harms (eg, perpetuating health inequity). …

… Biases inherent in electronic health records (EHRs), and … may significantly exacerbate
health inequities and challenge … of bias as those related to data, humans, and algorithms or …

… One of the most important domains of racial and economic inequalities is … Algorithms can
help reduce these inequalities because they are less likely than human doctors to make biased …

… Artificial intelligence (AI) algorithms are prone to bias at multiple stages of … biased models
may lead to patient harm due to action on inaccurate AI results or exacerbate health inequities …

… Specifically, we overview the different types of algorithmic bias, fairness quantification metrics,
and bias mitigation methods, and summarize popular software libraries and tools for bias …