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.

在病理学领域，人工智能 (AI) 在临床环境中的部署受到数据收集、模型透明度和可解释性方面的限制。在这里，我们描述了一个数字病理学框架 nuclei.io，它结合了主动学习和人机交互实时反馈，可快速创建不同的数据集和模型。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和病理学家之间的合作，包括子宫内膜活检中浆细胞的识别和淋巴结中结直肠癌转移的检测。在这两项研究中，nuclei.io 取得了显着的诊断性能改进。临床医生和人工智能之间的合作将通过提高准确性和效率来帮助数字病理学。