A pathologist–AI collaboration framework for enhancing diagnostic accuracies and efficiencies

IF 26.8 1区 医学 Q1 ENGINEERING, BIOMEDICAL
Zhi Huang, Eric Yang, Jeanne Shen, Dita Gratzinger, Frederick Eyerer, Brooke Liang, Jeffrey Nirschl, David Bingham, Alex M. Dussaq, Christian Kunder, Rebecca Rojansky, Aubre Gilbert, Alexandra L. Chang-Graham, Brooke E. Howitt, Ying Liu, Emily E. Ryan, Troy B. Tenney, Xiaoming Zhang, Ann Folkins, Edward J. Fox, Kathleen S. Montine, Thomas J. Montine, James Zou
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引用次数: 0

Abstract

In pathology, the deployment of artificial intelligence (AI) in clinical settings is constrained by limitations in data collection and in model transparency and interpretability. Here we describe a digital pathology framework, nuclei.io, that incorporates active learning and human-in-the-loop real-time feedback for the rapid creation of diverse datasets and models. We validate the effectiveness of the framework via two crossover user studies that leveraged collaboration between the AI and the pathologist, including the identification of plasma cells in endometrial biopsies and the detection of colorectal cancer metastasis in lymph nodes. In both studies, nuclei.io yielded considerable diagnostic performance improvements. Collaboration between clinicians and AI will aid digital pathology by enhancing accuracies and efficiencies.

Abstract Image

提高诊断准确性和效率的病理学家-人工智能合作框架
在病理学领域,人工智能(AI)在临床环境中的应用受到数据收集、模型透明度和可解释性的限制。在这里,我们描述了一个数字病理学框架 nuclei.io,该框架结合了主动学习和人环实时反馈,可快速创建各种数据集和模型。我们通过两项交叉用户研究验证了该框架的有效性,这些研究利用了人工智能和病理学家之间的合作,包括识别子宫内膜活检中的浆细胞和检测淋巴结中的结直肠癌转移。在这两项研究中,nuclei.io 都大大提高了诊断性能。临床医生与人工智能之间的合作将有助于提高数字病理学的准确性和效率。
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来源期刊
Nature Biomedical Engineering
Nature Biomedical Engineering Medicine-Medicine (miscellaneous)
CiteScore
45.30
自引率
1.10%
发文量
138
期刊介绍: Nature Biomedical Engineering is an online-only monthly journal that was launched in January 2017. It aims to publish original research, reviews, and commentary focusing on applied biomedicine and health technology. The journal targets a diverse audience, including life scientists who are involved in developing experimental or computational systems and methods to enhance our understanding of human physiology. It also covers biomedical researchers and engineers who are engaged in designing or optimizing therapies, assays, devices, or procedures for diagnosing or treating diseases. Additionally, clinicians, who make use of research outputs to evaluate patient health or administer therapy in various clinical settings and healthcare contexts, are also part of the target audience.
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