Explaining artificial intelligence with visual analytics in healthcare

IF 6.4 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery Pub Date : 2021-11-28 DOI:10.1002/widm.1427

Jeroen Ooge, G. Štiglic, K. Verbert

引用次数: 11

Abstract

To make predictions and explore large datasets, healthcare is increasingly applying advanced algorithms of artificial intelligence. However, to make well‐considered and trustworthy decisions, healthcare professionals require ways to gain insights in these algorithms' outputs. One approach is visual analytics, which integrates humans in decision‐making through visualizations that facilitate interaction with algorithms. Although many visual analytics systems have been developed for healthcare, a clear overview of their explanation techniques is lacking. Therefore, we review 71 visual analytics systems for healthcare, and analyze how they explain advanced algorithms through visualization, interaction, shepherding, and direct explanation. Based on our analysis, we outline research opportunities and challenges to further guide the exciting rapprochement of visual analytics and healthcare.

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用视觉分析解释医疗保健中的人工智能

为了进行预测和探索大型数据集，医疗保健越来越多地应用先进的人工智能算法。然而，为了做出经过深思熟虑和值得信赖的决策，医疗保健专业人员需要从这些算法的输出中获得见解。一种方法是视觉分析，它通过可视化来促进与算法的交互，将人类整合到决策中。尽管已经为医疗保健开发了许多可视化分析系统，但缺乏对其解释技术的清晰概述。因此，我们回顾了71个用于医疗保健的可视化分析系统，并分析了它们如何通过可视化、交互、引导和直接解释来解释高级算法。根据我们的分析，我们概述了研究的机遇和挑战，以进一步指导视觉分析和医疗保健的令人兴奋的和解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

22.70

自引率

2.60%

发文量

审稿时长

>12 weeks

期刊介绍： The goals of Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery (WIREs DMKD） are multifaceted. Firstly, the journal aims to provide a comprehensive overview of the current state of data mining and knowledge discovery by featuring ongoing reviews authored by leading researchers. Secondly, it seeks to highlight the interdisciplinary nature of the field by presenting articles from diverse perspectives, covering various application areas such as technology, business, healthcare, education, government, society, and culture. Thirdly, WIREs DMKD endeavors to keep pace with the rapid advancements in data mining and knowledge discovery through regular content updates. Lastly, the journal strives to promote active engagement in the field by presenting its accomplishments and challenges in an accessible manner to a broad audience. The content of WIREs DMKD is intended to benefit upper-level undergraduate and postgraduate students, teaching and research professors in academic programs, as well as scientists and research managers in industry.