Majority voting of doctors improves appropriateness of AI reliance in pathology

IF 5.3 2区 计算机科学 Q1 COMPUTER SCIENCE, CYBERNETICS
Hongyan Gu , Chunxu Yang , Shino Magaki , Neda Zarrin-Khameh , Nelli S. Lakis , Inma Cobos , Negar Khanlou , Xinhai R. Zhang , Jasmeet Assi , Joshua T. Byers , Ameer Hamza , Karam Han , Anders Meyer , Hilda Mirbaha , Carrie A. Mohila , Todd M. Stevens , Sara L. Stone , Wenzhong Yan , Mohammad Haeri , Xiang ‘Anthony’ Chen
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引用次数: 0

Abstract

As Artificial Intelligence (AI) making advancements in medical decision-making, there is a growing need to ensure doctors develop appropriate reliance on AI to avoid adverse outcomes. However, existing methods in enabling appropriate AI reliance might encounter challenges while being applied in the medical domain. With this regard, this work employs and provides the validation of an alternative approach – majority voting – to facilitate appropriate reliance on AI in medical decision-making. This is achieved by a multi-institutional user study involving 32 medical professionals with various backgrounds, focusing on the pathology task of visually detecting a pattern, mitoses, in tumor images. Here, the majority voting process was conducted by synthesizing decisions under AI assistance from a group of pathology doctors (pathologists). Two metrics were used to evaluate the appropriateness of AI reliance: Relative AI Reliance (RAIR) and Relative Self-Reliance (RSR). Results showed that even with groups of three pathologists, majority-voted decisions significantly increased both RAIR and RSR – by approximately 9% and 31%, respectively – compared to decisions made by one pathologist collaborating with AI. This increased appropriateness resulted in better precision and recall in the detection of mitoses. While our study is centered on pathology, we believe these insights can be extended to general high-stakes decision-making processes involving similar visual tasks.

医生的多数投票提高了病理学领域对人工智能的依赖程度
随着人工智能(AI)在医疗决策方面取得进展,确保医生适当依赖人工智能以避免不良后果的需求与日俱增。然而,现有的方法在医疗领域应用时可能会遇到挑战。有鉴于此,本研究采用了另一种方法--多数投票法--并对其进行了验证,以促进在医疗决策中适当依赖人工智能。为此,我们开展了一项多机构用户研究,涉及 32 名具有不同背景的医学专业人员,研究重点是病理学任务,即在肿瘤图像中视觉检测有丝分裂模式。在这项研究中,在一组病理学医生(病理学家)的人工智能协助下,通过综合决策进行了多数票表决。评估人工智能依赖性的适当性使用了两个指标:相对人工智能依赖度 (RAIR) 和相对自我依赖度 (RSR)。结果显示,即使是由三名病理学家组成的小组,与一名病理学家与人工智能合作做出的决定相比,多数票通过的决定也显著提高了 RAIR 和 RSR,分别提高了约 9% 和 31%。适当性的提高提高了有丝分裂检测的精确度和召回率。虽然我们的研究以病理学为中心,但我们相信这些见解可以推广到涉及类似视觉任务的一般高风险决策过程中。
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来源期刊
International Journal of Human-Computer Studies
International Journal of Human-Computer Studies 工程技术-计算机:控制论
CiteScore
11.50
自引率
5.60%
发文量
108
审稿时长
3 months
期刊介绍: The International Journal of Human-Computer Studies publishes original research over the whole spectrum of work relevant to the theory and practice of innovative interactive systems. The journal is inherently interdisciplinary, covering research in computing, artificial intelligence, psychology, linguistics, communication, design, engineering, and social organization, which is relevant to the design, analysis, evaluation and application of innovative interactive systems. Papers at the boundaries of these disciplines are especially welcome, as it is our view that interdisciplinary approaches are needed for producing theoretical insights in this complex area and for effective deployment of innovative technologies in concrete user communities. Research areas relevant to the journal include, but are not limited to: • Innovative interaction techniques • Multimodal interaction • Speech interaction • Graphic interaction • Natural language interaction • Interaction in mobile and embedded systems • Interface design and evaluation methodologies • Design and evaluation of innovative interactive systems • User interface prototyping and management systems • Ubiquitous computing • Wearable computers • Pervasive computing • Affective computing • Empirical studies of user behaviour • Empirical studies of programming and software engineering • Computer supported cooperative work • Computer mediated communication • Virtual reality • Mixed and augmented Reality • Intelligent user interfaces • Presence ...
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