HIT Webinar 特邀报告 | AI如何向医生解释？以临床用户为中心的可解释人工智能

△ 金博士个人网站：https://weina.me/

△ 书籍推荐

张钹院士作序，沈向洋、周志华倾情推荐。杨强、范力欣、朱军、陈一昕、张拳石、朱松纯、陶大程、崔鹏、周少华、刘琦、黄萱菁、张永锋等顶级专家扛鼎之作。

△ 电子书推荐

https://christophm.github.io/interpretable-ml-book/

△ 论文推荐

https://ieeexplore.ieee.org/document/9233366

Recently, artificial intelligence and machine learning in general have demonstrated remarkable performances in many tasks, from image processing to natural language processing, especially with the advent of deep learning (DL). Along with research progress, they have encroached upon many different fields and disciplines. Some of them require high level of accountability and thus transparency, for example, the medical sector. Explanations for machine decisions and predictions are thus needed to justify their reliability. This requires greater interpretability, which often means we need to understand the mechanism underlying the algorithms. Unfortunately, the blackbox nature of the DL is still unresolved, and many machine decisions are still poorly understood. We provide a review on interpretabilities suggested by different research works and categorize them. The different categories show different dimensions in interpretability research, from approaches that provide “obviously” interpretable information to the studies of complex patterns. By applying the same categorization to interpretability in medical research, it is hoped that: 1) clinicians and practitioners can subsequently approach these methods with caution; 2) insight into interpretability will be born with more considerations for medical practices; and 3) initiatives to push forward data-based, mathematically grounded, and technically grounded medical education are encouraged.

△ 论文推荐

https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-020-01332-6

Explainability is one of the most heavily debated topics when it comes to the application of artificial intelligence (AI) in healthcare. Even though AI-driven systems have been shown to outperform humans in certain analytical tasks, the lack of explainability continues to spark criticism. Yet, explainability is not a purely technological issue, instead it invokes a host of medical, legal, ethical, and societal questions that require thorough exploration. This paper provides a comprehensive assessment of the role of explainability in medical AI and makes an ethical evaluation of what explainability means for the adoption of AI-driven tools into clinical practice.