AUTHOR=Bruckert Sebastian , Finzel Bettina , Schmid Ute TITLE=The Next Generation of Medical Decision Support: A Roadmap Toward Transparent Expert Companions JOURNAL=Frontiers in Artificial Intelligence VOLUME=3 YEAR=2020 URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2020.507973 DOI=10.3389/frai.2020.507973 ISSN=2624-8212 ABSTRACT=
Increasing quality and performance of artificial intelligence (AI) in general and machine learning (ML) in particular is followed by a wider use of these approaches in everyday life. As part of this development, ML classifiers have also gained more importance for diagnosing diseases within biomedical engineering and medical sciences. However, many of those ubiquitous high-performing ML algorithms reveal a black-box-nature, leading to opaque and incomprehensible systems that complicate human interpretations of single predictions or the whole prediction process. This puts up a serious challenge on human decision makers to develop trust, which is much needed in life-changing decision tasks. This paper is designed to answer the question how expert companion systems for decision support can be designed to be interpretable and therefore transparent and comprehensible for humans. On the other hand, an approach for interactive ML as well as human-in-the-loop-learning is demonstrated in order to integrate human expert knowledge into ML models so that humans and machines act as companions within a critical decision task. We especially address the problem of