机器学习和医学成像中的偏差:机遇和挑战
Amey Vrudhula1,2, Alan C Kwan2, David Ouyang2,3
1Icahn School of Medicine at Mount Sinai, New York (A.V.).
Circulation. Cardiovascular imaging
|February 20, 2024
概括
医学成像中的机器学习 (ML) 可以减少医疗偏见并改善诊断. 然而,仔细设计至关重要,以防止ML模型延续现有的健康差异.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 健康 公平 卫生 公平
背景情况:
- 医疗保健偏见导致弱势群体的结果更差.
- 医学成像对于诊断至关重要,但在访问,获取和解释方面容易产生偏见.
- 机器学习 (ML) 具有提高诊断准确度和识别新疾病模式的潜力.
结论:
- ML在减少医学成像偏差和改善患者护理方面具有显著的前景.
- 需要考虑战略设计,以最大限度地提高ML的好处,同时最大限度地降低风险.
- 目标是利用ML为所有患者提供公平的医疗保健改进.
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