Explainable AI and Multi-Modal Causability in Medicine.
Explainable AI and Multi-Modal Causability in Medicine.
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DOI：
10.1515/icom-2020-0024
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发表时间：
2021-01-26
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影响因子：
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通讯作者：
Holzinger, Andreas
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作者：
Holzinger, Andreas
统计机器学习的进步使人工智能在医学上取得了成功，在某些分类任务中甚至超越了人类水平的表现。然而，相关性并不是因果关系，成功的模式往往是复杂的“黑箱”，这使得人们难以理解为什么会取得这样的结果。可解释的人工智能（xAI）社区开发了一些方法，例如。强调哪些输入参数与结果相关;然而，在医学领域需要因果关系：与可用性包括使用质量的测量一样，因果关系包括xAI产生的解释质量的测量。未来人机界面的关键是将可解释性与因果性相映射，并允许领域专家提出问题以理解AI为什么会得出结果，并提出“假设”问题（反事实）以深入了解结果的潜在独立解释因素。多模态因果关系在医学领域中很重要，因为通常不同的模态会导致结果。
Progress in statistical machine learning made AI in medicine successful, in certain classification tasks even beyond human level performance. Nevertheless, correlation is not causation and successful models are often complex “black-boxes”, which make it hard to understand why a result has been achieved. The explainable AI (xAI) community develops methods, e. g. to highlight which input parameters are relevant for a result; however, in the medical domain there is a need for causability: In the same way that usability encompasses measurements for the quality of use, causability encompasses measurements for the quality of explanations produced by xAI. The key for future human-AI interfaces is to map explainability with causability and to allow a domain expert to ask questions to understand why an AI came up with a result, and also to ask “what-if” questions (counterfactuals) to gain insight into the underlying independent explanatory factors of a result. A multi-modal causability is important in the medical domain because often different modalities contribute to a result.