Huang Zhi, Yang Eric, Shen Jeanne, Gratzinger Dita, Eyerer Frederick, Liang Brooke, Nirschl Jeffrey, Bingham David, Dussaq Alex M, Kunder Christian, Rojansky Rebecca, Gilbert Aubre, Chang-Graham Alexandra L, Howitt Brooke E, Liu Ying, Ryan Emily E, Tenney Troy B, Zhang Xiaoming, Folkins Ann, Fox Edward J, Montine Kathleen S, Montine Thomas J, Zou James
Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA.
Nat Biomed Eng. 2025 Apr;9(4):455-470. doi: 10.1038/s41551-024-01223-5. Epub 2024 Jun 19.
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.
在病理学中，人工智能（AI）在临床环境中的应用受到数据收集以及模型透明度和可解释性方面的限制。在此，我们描述了一个数字病理学框架nuclei.io，该框架纳入了主动学习和人工参与的实时反馈，用于快速创建多样化的数据集和模型。我们通过两项交叉用户研究验证了该框架的有效性，这两项研究利用了人工智能与病理学家之间的协作，包括子宫内膜活检中浆细胞的识别以及淋巴结中结直肠癌转移的检测。在这两项研究中，nuclei.io都带来了显著的诊断性能提升。临床医生与人工智能之间的协作将通过提高准确性和效率来助力数字病理学发展。
