随着人工智能(AI)系统数量的不断增加,减轻与使用相关的风险已成为最紧迫的科学和社会问题之一。为此,欧盟通过了欧盟人工智能法案,提出解决方案策略,这些策略可以概括为“可信度”。在抗癌药物敏感性预测中,机器学习(ML)方法被开发用于医疗决策支持系统中,这需要非凡的可信度。这篇综述概述了ML抗癌药物敏感性预测方法的前景。包括对四个主要ML领域的简要介绍(监督,无人监督,半监督,和强化学习)。特别是,我们解决了在多大程度上与可信度相关的属性的问题,更具体地说,可解释性和可靠性,在过去的十年中,已被纳入抗癌药物敏感性预测方法。总的来说,我们分析了36篇具有抗癌药物敏感性预测方法的论文。我们的结果表明,到目前为止,对可靠性的需求几乎没有得到解决。可解释性,另一方面,经常被考虑用于模型开发。然而,这个概念很直观地使用,缺乏明确的定义。因此,我们提出了一种易于扩展的可解释性分类法,统一在该领域内明确或隐含地使用的所有普遍内涵。
With the ever-increasing number of artificial intelligence (AI) systems, mitigating risks associated with their use has become one of the most urgent scientific and societal issues. To this end, the European Union passed the EU AI Act, proposing solution strategies that can be summarized under the umbrella term
trustworthiness. In anti-cancer drug sensitivity prediction, machine learning (ML) methods are developed for application in medical decision support systems, which require an extraordinary level of
trustworthiness. This review offers an overview of the ML landscape of methods for anti-cancer drug sensitivity prediction, including a brief introduction to the four major ML realms (supervised, unsupervised, semi-supervised, and reinforcement learning). In particular, we address the question to what extent
trustworthiness-related properties, more specifically, interpretability and reliability, have been incorporated into anti-cancer drug sensitivity prediction methods over the previous decade. In total, we analyzed 36 papers with approaches for anti-cancer drug sensitivity prediction. Our results indicate that the need for reliability has hardly been addressed so far. Interpretability, on the other hand, has often been considered for model development. However, the concept is rather used intuitively, lacking clear definitions. Thus, we propose an easily extensible taxonomy for interpretability, unifying all prevalent connotations explicitly or implicitly used within the field.