{"title":"Research trends on AI in breast cancer diagnosis, and treatment over two decades.","authors":"Alok Singh, Akanksha Singh, Sudip Bhattacharya","doi":"10.1007/s12672-024-01671-0","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>Recently, the integration of Artificial Intelligence (AI) has significantly enhanced the diagnostic accuracy in breast cancer screening. This study aims to deliver an extensive review of the advancements in AI for breast cancer diagnosis and prognosis through a bibliometric analysis.</p><p><strong>Methodology: </strong>Therefore, this study gathered pertinent peer-reviewed research articles from the Scopus database, spanning the years 2000 to 2024. These articles were subsequently subjected to quantitative analysis and visualization through the Bibliometrix R package. Ultimately, potential areas for future research challenges were pinpointed.</p><p><strong>Results: </strong>This study analyzes the development of Artificial Intelligence (AI) research for breast cancer diagnosis and prognosis from 2000 to 2024, based on 2678 publications sourced from Scopus. A sharp rise in global publication trends is observed between 2018 and 2023, with 2023 producing 456 papers, indicating intensified academic focus. Leading contributors include ZHENG B, with 36 publications, and institutions like RADBOUD UNIVERSITY MEDICAL CENTER and the IEO EUROPEAN INSTITUTE OF ONCOLOGY IRCCS. The USA leads both in publications (473) and total citations (18,530), followed by India with 289 papers. Co-occurrence analysis shows that \"mammography\" (3171 occurrences) and \"artificial intelligence\" (1691 occurrences) are among the most frequent keywords, reflecting core themes. Co-citation network analysis identifies foundational works by authors like Lecun Y. and Simonyan K. in advancing AI applications in breast cancer. Institutional and country-level collaboration analysis reveals the USA's significant partnerships with China, the UK, and Canada, driving the global research agenda in this field.</p><p><strong>Conclusion: </strong>In conclusion, this bibliometric review underscores the growing influence of AI, particularly deep learning, in breast cancer diagnosis and treatment research from 2000 to 2024. The United States leads the field in publications and collaborations, with India, Spain, and the Netherlands also making significant contributions. Key institutions and journals have driven advancements, with AI applications focusing on improving diagnostic imaging and early detection. However, challenges like data limitations, regulatory hurdles, and unequal global collaboration persist, requiring further interdisciplinary efforts to enhance AI integration in clinical practice.</p>","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":"15 1","pages":"772"},"PeriodicalIF":2.8000,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11655727/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Discover. Oncology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s12672-024-01671-0","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
引用次数: 0
Abstract
Objective: Recently, the integration of Artificial Intelligence (AI) has significantly enhanced the diagnostic accuracy in breast cancer screening. This study aims to deliver an extensive review of the advancements in AI for breast cancer diagnosis and prognosis through a bibliometric analysis.
Methodology: Therefore, this study gathered pertinent peer-reviewed research articles from the Scopus database, spanning the years 2000 to 2024. These articles were subsequently subjected to quantitative analysis and visualization through the Bibliometrix R package. Ultimately, potential areas for future research challenges were pinpointed.
Results: This study analyzes the development of Artificial Intelligence (AI) research for breast cancer diagnosis and prognosis from 2000 to 2024, based on 2678 publications sourced from Scopus. A sharp rise in global publication trends is observed between 2018 and 2023, with 2023 producing 456 papers, indicating intensified academic focus. Leading contributors include ZHENG B, with 36 publications, and institutions like RADBOUD UNIVERSITY MEDICAL CENTER and the IEO EUROPEAN INSTITUTE OF ONCOLOGY IRCCS. The USA leads both in publications (473) and total citations (18,530), followed by India with 289 papers. Co-occurrence analysis shows that "mammography" (3171 occurrences) and "artificial intelligence" (1691 occurrences) are among the most frequent keywords, reflecting core themes. Co-citation network analysis identifies foundational works by authors like Lecun Y. and Simonyan K. in advancing AI applications in breast cancer. Institutional and country-level collaboration analysis reveals the USA's significant partnerships with China, the UK, and Canada, driving the global research agenda in this field.
Conclusion: In conclusion, this bibliometric review underscores the growing influence of AI, particularly deep learning, in breast cancer diagnosis and treatment research from 2000 to 2024. The United States leads the field in publications and collaborations, with India, Spain, and the Netherlands also making significant contributions. Key institutions and journals have driven advancements, with AI applications focusing on improving diagnostic imaging and early detection. However, challenges like data limitations, regulatory hurdles, and unequal global collaboration persist, requiring further interdisciplinary efforts to enhance AI integration in clinical practice.
目的:近年来,人工智能(AI)的融合显著提高了乳腺癌筛查的诊断准确性。本研究旨在通过文献计量分析,对人工智能在乳腺癌诊断和预后方面的进展进行广泛的回顾。方法:因此,本研究从Scopus数据库中收集了相关的同行评审研究文章,时间跨度为2000年至2024年。这些文章随后通过Bibliometrix R软件包进行定量分析和可视化。最后,确定了未来研究挑战的潜在领域。结果:本研究基于来自Scopus的2678篇论文,分析了2000年至2024年人工智能(AI)在乳腺癌诊断和预后方面的研究进展。2018年至2023年期间,全球论文发表趋势急剧上升,2023年发表论文456篇,表明学术关注度增强。主要贡献者包括ZHENG B,发表了36篇论文,以及RADBOUD UNIVERSITY MEDICAL CENTER和IEO EUROPEAN INSTITUTE OF ONCOLOGY IRCCS等机构。美国在发表论文(473篇)和总引用数(18530篇)方面都领先,其次是印度,有289篇论文。共现分析显示,“乳房x光检查”(3171次)和“人工智能”(1691次)是出现频率最高的关键词,反映了核心主题。共引网络分析确定了Lecun Y.和Simonyan K.等作者在推进人工智能在乳腺癌中的应用方面的基础性工作。机构和国家层面的合作分析揭示了美国与中国、英国和加拿大的重要伙伴关系,推动了该领域的全球研究议程。结论:总而言之,这篇文献计量学综述强调了人工智能,特别是深度学习,在2000年至2024年乳腺癌诊断和治疗研究中的影响力越来越大。美国在该领域的出版物和合作方面处于领先地位,印度、西班牙和荷兰也作出了重大贡献。关键机构和期刊推动了人工智能的进步,人工智能应用的重点是改善诊断成像和早期检测。然而,诸如数据限制、监管障碍和不平等的全球合作等挑战仍然存在,需要进一步的跨学科努力来加强人工智能在临床实践中的整合。