Algorithm fairness in ai for medicine and healthcare
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
evaluation of AI models stratified across race sub-populations have revealed inequalities in
how patients are diagnosed, given treatments, and billed for healthcare costs. In this
perspective article, we summarize the intersectional field of fairness in machine learning
through the context of current issues in healthcare, outline how algorithmic biases (eg-image …
healthcare, algorithm fairness is a challenging problem in delivering equitable care. Recent
evaluation of AI models stratified across race sub-populations have revealed inequalities in
how patients are diagnosed, given treatments, and billed for healthcare costs. In this
perspective article, we summarize the intersectional field of fairness in machine learning
through the context of current issues in healthcare, outline how algorithmic biases (eg-image …
[PDF][PDF] Algorithm Fairness in AI for Medicine and Healthcare
F Mahmood - arXiv preprint arXiv:2110.00603, 2021 - academia.edu
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
evaluation of AI models stratified across race sub-populations have revealed inequalities in
how patients are diagnosed, given treatments, and billed for healthcare costs. In this
perspective article, we summarize the intersectional field of fairness in machine learning
through the context of current issues in healthcare, outline how algorithmic biases (eg-image …
healthcare, algorithm fairness is a challenging problem in delivering equitable care. Recent
evaluation of AI models stratified across race sub-populations have revealed inequalities in
how patients are diagnosed, given treatments, and billed for healthcare costs. In this
perspective article, we summarize the intersectional field of fairness in machine learning
through the context of current issues in healthcare, outline how algorithmic biases (eg-image …
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