基于人工智能的肿瘤病理学的全球研究趋势和重点：一项科学计量学研究。

BACKGROUND: With the development of digital pathology and the renewal of deep learning algorithm, artificial intelligence (AI) is widely applied in tumor pathology. Previous researches have demonstrated that AI-based tumor pathology may help to solve the challenges faced by traditional pathology. This technology has attracted the attention of scholars in many fields and a large amount of articles have been published. This study mainly summarizes the knowledge structure of AI-based tumor pathology through bibliometric analysis, and discusses the potential research trends and foci. METHODS: Publications related to AI-based tumor pathology from 1999 to 2021 were selected from Web of Science Core Collection. VOSviewer and Citespace were mainly used to perform and visualize co-authorship, co-citation, and co-occurrence analysis of countries, institutions, authors, references and keywords in this field. RESULTS: A total of 2753 papers were included. The papers on AI-based tumor pathology research had been continuously increased since 1999. The United States made the largest contribution in this field, in terms of publications (1138, 41.34%), H-index (85) and total citations (35,539 times). We identified the most productive institution and author were Harvard Medical School and Madabhushi Anant, while Jemal Ahmedin was the most co-cited author. Scientific Reports was the most prominent journal and after analysis, Lecture Notes in Computer Science was the journal with highest total link strength. According to the result of references and keywords analysis, "breast cancer histopathology" "convolutional neural network" and "histopathological image" were identified as the major future research foci. CONCLUSIONS: AI-based tumor pathology is in the stage of vigorous development and has a bright prospect. International transboundary cooperation among countries and institutions should be strengthened in the future. It is foreseeable that more research foci will be lied in the interpretability of deep learning-based model and the development of multi-modal fusion model.

背景：随着数字病理学的发展和深度学习算法的更新，人工智能（AI）广泛应用于肿瘤病理学。先前的研究表明，基于 AI 的肿瘤病理学可能有助于解决传统病理学面临的挑战。这项技术引起了许多领域学者的关注，发表了大量文章。本研究主要通过文献计量分析总结基于 AI 的肿瘤病理学的知识结构，并探讨潜在的研究趋势和重点。

方法：从 Web of Science 核心合集选取 1999 年至 2021 年与基于 AI 的肿瘤病理学相关的文献。主要使用 VOSviewer 和 Citespace 对该领域的国家、机构、作者、参考文献和关键词的合著、共引和共现分析进行分析和可视化。

结果：共纳入 2753 篇论文。自 1999 年以来，基于 AI 的肿瘤病理学研究论文持续增加。美国在该领域的发文量（1138 篇，占 41.34%）、H 指数（85）和总被引频次（35539 次）最大。确定最具生产力的机构和作者分别是哈佛医学院和 Madabhushi Anant，而被引频次最高的作者是 Jemal Ahmedin。《Scientific Reports》是最突出的期刊，经过分析，《Lecture Notes in Computer Science》是总链接强度最高的期刊。根据参考文献和关键词分析的结果，“乳腺癌组织病理学”“卷积神经网络”和“组织病理学图像”被确定为未来的主要研究重点。

结论：基于 AI 的肿瘤病理学正处于蓬勃发展阶段，前景光明。未来应加强国家和机构之间的国际跨界合作。可以预见，更多的研究重点将在于基于深度学习模型的可解释性和多模态融合模型的开发。