精准医学中的AI/ML:超越作之外的看法
Zhiheng Xu1, Bipasa Biswas2, Lin Li3
1U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD, 20993, USA. Zhiheng.Xu@fda.hhs.gov.
Therapeutic innovation & regulatory science
|June 13, 2023
概括
人工智能 (AI) 和机器学习 (ML) 在精准医学中提供了巨大的潜力,但在药物发现和诊断等领域的成功应用中,必须解决诸如偏见和数据质量等挑战.
科学领域:
- 生物制药研究 生物制药研究
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
背景情况:
- 人工智能 (AI) 和机器学习 (ML) 在医学研究中越来越突出.
- 应用范围包括药物发现,数字成像,诊断,基因检测和个性化护理途径.
- 在医疗保健中,区分真正的好处与作对于AI/ML至关重要.
研究的目的:
- 总结和扩大有关AI/ML在精准医学方面的专家讨论.
- 确定在医疗保健中应用AI/ML的关键挑战和潜在解决方案.
- 为了区分AI/ML的潜力与其当前的,实现的好处.
主要方法:
- 专家小组讨论在2022年美国统计学会生物制药部门的监管行业统计工作坊.
- 综合了包括AI/ML应用,偏见和数据质量在内的小组主题.
- 扩展讨论的挑战和克服它们的策略.
主要成果:
- 精准医学中的AI/ML应用面临着重大障碍.
- 人工智能/ML算法的偏差和数据质量是关键问题.
- 专家共识强调需要仔细验证和实施.
结论:
- 在精准医学中成功整合AI/ML需要解决数据质量和算法偏差.
- 需要进一步的研究和监管明确,才能充分利用AI/ML的全部潜力.
- 平衡的观点,将潜力与作区分开来,对于负责任的AI/ML采用至关重要.
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