[论文解读] Explainable AI applications in the Medical Domain: a systematic review

Nicoletta Prentzas, Antonis Kakas+1 more|arXiv (Cornell University)|Aug 10, 2023

Artificial Intelligence in Healthcare and Education被引用 19

一句话总结

对医疗决策支持中的可解释性人工智能（XAI）解决方案的系统性综述，分析198篇最近文章以识别趋势、技术和差距。

ABSTRACT

Artificial Intelligence in Medicine has made significant progress with emerging applications in medical imaging, patient care, and other areas. While these applications have proven successful in retrospective studies, very few of them were applied in practice.The field of Medical AI faces various challenges, in terms of building user trust, complying with regulations, using data ethically.Explainable AI (XAI) aims to enable humans understand AI and trust its results. This paper presents a literature review on the recent developments of XAI solutions for medical decision support, based on a representative sample of 198 articles published in recent years. The systematic synthesis of the relevant articles resulted in several findings. (1) model-agnostic XAI techniques were mostly employed in these solutions, (2) deep learning models are utilized more than other types of machine learning models, (3) explainability was applied to promote trust, but very few works reported the physicians participation in the loop, (4) visual and interactive user interface is more useful in understanding the explanation and the recommendation of the system. More research is needed in collaboration between medical and AI experts, that could guide the development of suitable frameworks for the design, implementation, and evaluation of XAI solutions in medicine.

研究动机与目标

• 激发并绘制XAI在医疗决策支持及相关领域中的应用图谱。
• 识别在医疗AI应用中使用的主流XAI技术与模型类型。
• 评估可解释性如何促进信任、用户交互和临床整合。
• 突出上述差距，如医生参与度以及医疗与AI专家之间的协作。

提出的方法

• 从近年的文献中进行具有代表性的抽样，涵盖198篇文章。
• 综合模型无关XAI技术和深度学习模型的流行程度的发现。
• 评估可解释性在信任、医生参与和UI设计中的作用。

实验结果

研究问题

• RQ1 在医疗决策支持中最常使用的XAI技术有哪些？
• RQ2 哪些模型族（如深度学习）在XAI医疗应用中占主导？
• RQ3 可解释性如何用于促进信任和临床医生的接受度？
• RQ4 医生是否积极参与XAI开发环节，UI/UX如何支持理解？
• RQ5 已识别的差距以及医疗与AI专家未来的协作需求是什么？

主要发现

• 模型无关的XAI技术在医疗XAI解决方案中被频繁使用。
• 在这些应用中，深度学习模型的使用比其他机器学习模型更多。
• 可解释性通常用来促进信任，但医生参与循环相对有限。
• 视觉化和交互式用户界面对理解解释和建议更有帮助。