机器学习在医学中的应用和问题:用可解释的AI弥合差距
Kenji Karako1, Wei Tang1,2
1Hepato-Biliary-Pancreatic Surgery Division, Department of Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Bioscience trends
|December 8, 2024
概括
机器学习 (ML) 提供了强大的医学预测,但其"黑子"性质是一个挑战. 可解释AI (XAI) 对于医疗保健中的透明和可信赖的ML应用至关重要,改善患者的治疗结果.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 计算生物学 计算生物学
背景情况:
- 机器学习 (ML),特别是深度学习,在医疗应用中显示出巨大的潜力.
- 像卷积神经网络和变压器这样的技术为复杂的医疗问题提供了高性能预测.
- ML有助于分析患者数据,用于预测并发症和评估疾病风险等任务.
研究的目的:
- 探索ML在医学中的多样化应用.
- 描述在医疗保健中使用的流行ML算法.
- 突出可解释AI (XAI) 在解决"黑子"问题的重要性和方法.
主要方法:
- 对医学中ML应用的当前文献的综述.
- 描述常见的ML算法 (例如神经网络,变压器).
- 讨论XAI技术,以提高模型的可解释性.
主要成果:
- 机器学习模型越来越多地用于医学决策,风险评估和结果预测.
- 机器学习模型缺乏透明度 ("黑子"问题) 构成了临床采用障碍.
- XAI提供方法,提供对ML模型预测的洞察力,培养信任和理解.
结论:
- 可解释性AI (XAI) 对于ML在医疗保健中的伦理和实际整合至关重要.
- 通过XAI提高ML的解释性可以提高临床医生和患者的信任.
- 通过使ML模型易于理解,XAI促进了个性化的治疗策略和更好的患者结果.
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