不确定性量化和可解释的人工智能在骨科成像:一个及时的呼吁采取行动
Ahmad P Tafti1,2,3, Qiangqiang Gu3, Johannes F Plate4
1School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA, USA.
Journal of clinical orthopaedics and trauma
|October 15, 2025
概括
在骨科中,人工智能 (AI) 需要的不仅仅是准确性. 整合不确定性量化和可解释的人工智能对于可信的人工智能至关重要,确保更安全的临床采用和改善患者结果.
科学领域:
- 整形外科成像 整形外科成像
- 人工智能的人工智能
- 医疗人工智能 医疗人工智能
背景情况:
- 深度学习模型在骨科成像任务中显示出高精度,例如骨折检测和骨关节炎分级.
- 人工智能在骨科中的临床信任和采用受到阻碍,因为仅仅精确性是不够的.
- 当前的人工智能模型往往缺乏透明度,在不解释推理或量化不确定性的情况下提供预测.
研究的目的:
- 倡导在骨科成像中整合不确定性量化和可解释的AI.
- 突出这些进展对于临床信任和安全采用的必要性.
- 弥合人工智能创新和实际的骨科工作流程之间的差距.
主要方法:
- 讨论不确定性量化在识别不可靠的人工智能预测中的作用.
- 解释了可解释AI (XAI) 技术如何提高AI模型推理的透明度.
- 强调了这些方法在可信的人工智能方面的综合潜力.
主要成果:
- 不确定性量化可以促使人工智能预测进行确认测试或人类监督.
- 可解释的人工智能使外科医生和放射科医生能够更好地解释AI输出.
- 这些方法的整合使人工智能超越了准确性,转向了问责制.
结论:
- 意识到不确定性和可解释的人工智能对于骨科领域的可信的人工智能至关重要.
- 这些进展对于将人工智能安全整合到临床实践中至关重要.
- 骨科界现在必须采取行动,接受这些关键的人工智能组件.
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