乳腺放射学中人工智能被引用次数最多的100篇文章：文献计量分析。

IF 4.1 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Insights into Imaging Pub Date : 2024-12-12 DOI:10.1186/s13244-024-01869-4

Sneha Singh, Nuala A Healy

{"title":"乳腺放射学中人工智能被引用次数最多的100篇文章：文献计量分析。","authors":"Sneha Singh, Nuala A Healy","doi":"10.1186/s13244-024-01869-4","DOIUrl":null,"url":null,"abstract":"Introduction: Artificial intelligence (AI) in radiology is a rapidly evolving field. In breast imaging, AI has already been applied in a real-world setting and multiple studies have been conducted in the area. The aim of this analysis is to identify the most influential publications on the topic of artificial intelligence in breast imaging.Methods: A retrospective bibliometric analysis was conducted on artificial intelligence in breast radiology using the Web of Science database. The search strategy involved searching for the keywords 'breast radiology' or 'breast imaging' and the various keywords associated with AI such as 'deep learning', 'machine learning,' and 'neural networks'.Results: From the top 100 list, the number of citations per article ranged from 30 to 346 (average 85). The highest cited article titled 'Artificial Neural Networks In Mammography-Application To Decision-Making In The Diagnosis Of Breast-Cancer' was published in Radiology in 1993. Eighty-three of the articles were published in the last 10 years. The journal with the greatest number of articles was Radiology (n = 22). The most common country of origin was the United States (n = 51). Commonly occurring topics published were the use of deep learning models for breast cancer detection in mammography or ultrasound, radiomics in breast cancer, and the use of AI for breast cancer risk prediction.Conclusion: This study provides a comprehensive analysis of the top 100 most-cited papers on the subject of artificial intelligence in breast radiology and discusses the current most influential papers in the field.Clinical relevance statement: This article provides a concise summary of the top 100 most-cited articles in the field of artificial intelligence in breast radiology. It discusses the most impactful articles and explores the recent trends and topics of research in the field.Key points: Multiple studies have been conducted on AI in breast radiology. The most-cited article was published in the journal Radiology in 1993. This study highlights influential articles and topics on AI in breast radiology.","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"297"},"PeriodicalIF":4.1000,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11638451/pdf/","citationCount":"0","resultStr":"{\"title\":\"The top 100 most-cited articles on artificial intelligence in breast radiology: a bibliometric analysis.\",\"authors\":\"Sneha Singh, Nuala A Healy\",\"doi\":\"10.1186/s13244-024-01869-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Introduction: Artificial intelligence (AI) in radiology is a rapidly evolving field. In breast imaging, AI has already been applied in a real-world setting and multiple studies have been conducted in the area. The aim of this analysis is to identify the most influential publications on the topic of artificial intelligence in breast imaging.Methods: A retrospective bibliometric analysis was conducted on artificial intelligence in breast radiology using the Web of Science database. The search strategy involved searching for the keywords 'breast radiology' or 'breast imaging' and the various keywords associated with AI such as 'deep learning', 'machine learning,' and 'neural networks'.Results: From the top 100 list, the number of citations per article ranged from 30 to 346 (average 85). The highest cited article titled 'Artificial Neural Networks In Mammography-Application To Decision-Making In The Diagnosis Of Breast-Cancer' was published in Radiology in 1993. Eighty-three of the articles were published in the last 10 years. The journal with the greatest number of articles was Radiology (n = 22). The most common country of origin was the United States (n = 51). Commonly occurring topics published were the use of deep learning models for breast cancer detection in mammography or ultrasound, radiomics in breast cancer, and the use of AI for breast cancer risk prediction.Conclusion: This study provides a comprehensive analysis of the top 100 most-cited papers on the subject of artificial intelligence in breast radiology and discusses the current most influential papers in the field.Clinical relevance statement: This article provides a concise summary of the top 100 most-cited articles in the field of artificial intelligence in breast radiology. It discusses the most impactful articles and explores the recent trends and topics of research in the field.Key points: Multiple studies have been conducted on AI in breast radiology. The most-cited article was published in the journal Radiology in 1993. This study highlights influential articles and topics on AI in breast radiology.\",\"PeriodicalId\":13639,\"journal\":{\"name\":\"Insights into Imaging\",\"volume\":\"15 1\",\"pages\":\"297\"},\"PeriodicalIF\":4.1000,\"publicationDate\":\"2024-12-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11638451/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Insights into Imaging\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s13244-024-01869-4\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Insights into Imaging","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s13244-024-01869-4","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}

引用次数: 0

摘要

人工智能（AI）在放射学中的应用是一个快速发展的领域。在乳房成像方面，人工智能已经应用于现实世界，并在该领域进行了多项研究。本分析的目的是确定人工智能在乳腺成像领域最具影响力的出版物。方法：利用Web of Science数据库对人工智能在乳腺放射学中的应用进行回顾性文献计量分析。搜索策略包括搜索关键词“乳房放射学”或“乳房成像”，以及与人工智能相关的各种关键词，如“深度学习”、“机器学习”和“神经网络”。结果：在前100名中，每篇文章被引用次数在30 ~ 346次之间（平均85次）。引用率最高的文章《乳腺x线摄影中的人工神经网络——乳腺癌诊断决策中的应用》发表于1993年的《放射学》。其中83篇文章是在过去10年里发表的。发表文章最多的期刊是Radiology （n = 22）。最常见的原产国是美国（n = 51）。发表的常见主题是在乳房x光检查或超声波中使用深度学习模型进行乳腺癌检测，在乳腺癌中使用放射组学，以及在乳腺癌风险预测中使用人工智能。结论：本研究对乳腺放射学人工智能领域前100篇被引论文进行了综合分析，并讨论了当前该领域最具影响力的论文。临床相关性声明：本文简要总结了乳腺放射学人工智能领域被引用次数最多的前100篇文章。它讨论了最具影响力的文章，并探讨了该领域研究的最新趋势和主题。重点：人工智能在乳腺放射学中的应用已经进行了多项研究。被引用最多的文章发表在1993年的《放射学》杂志上。本研究重点介绍了人工智能在乳腺放射学方面有影响力的文章和主题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

The top 100 most-cited articles on artificial intelligence in breast radiology: a bibliometric analysis.

Introduction: Artificial intelligence (AI) in radiology is a rapidly evolving field. In breast imaging, AI has already been applied in a real-world setting and multiple studies have been conducted in the area. The aim of this analysis is to identify the most influential publications on the topic of artificial intelligence in breast imaging.

Methods: A retrospective bibliometric analysis was conducted on artificial intelligence in breast radiology using the Web of Science database. The search strategy involved searching for the keywords 'breast radiology' or 'breast imaging' and the various keywords associated with AI such as 'deep learning', 'machine learning,' and 'neural networks'.

Results: From the top 100 list, the number of citations per article ranged from 30 to 346 (average 85). The highest cited article titled 'Artificial Neural Networks In Mammography-Application To Decision-Making In The Diagnosis Of Breast-Cancer' was published in Radiology in 1993. Eighty-three of the articles were published in the last 10 years. The journal with the greatest number of articles was Radiology (n = 22). The most common country of origin was the United States (n = 51). Commonly occurring topics published were the use of deep learning models for breast cancer detection in mammography or ultrasound, radiomics in breast cancer, and the use of AI for breast cancer risk prediction.

Conclusion: This study provides a comprehensive analysis of the top 100 most-cited papers on the subject of artificial intelligence in breast radiology and discusses the current most influential papers in the field.

Clinical relevance statement: This article provides a concise summary of the top 100 most-cited articles in the field of artificial intelligence in breast radiology. It discusses the most impactful articles and explores the recent trends and topics of research in the field.

Key points: Multiple studies have been conducted on AI in breast radiology. The most-cited article was published in the journal Radiology in 1993. This study highlights influential articles and topics on AI in breast radiology.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Insights into Imaging Medicine-Radiology, Nuclear Medicine and Imaging

CiteScore

7.30

自引率

4.30%

发文量

182

审稿时长

13 weeks

期刊介绍： Insights into Imaging (I³) is a peer-reviewed open access journal published under the brand SpringerOpen. All content published in the journal is freely available online to anyone, anywhere! I³ continuously updates scientific knowledge and progress in best-practice standards in radiology through the publication of original articles and state-of-the-art reviews and opinions, along with recommendations and statements from the leading radiological societies in Europe. Founded by the European Society of Radiology (ESR), I³ creates a platform for educational material, guidelines and recommendations, and a forum for topics of controversy. A balanced combination of review articles, original papers, short communications from European radiological congresses and information on society matters makes I³ an indispensable source for current information in this field. I³ is owned by the ESR, however authors retain copyright to their article according to the Creative Commons Attribution License (see Copyright and License Agreement). All articles can be read, redistributed and reused for free, as long as the author of the original work is cited properly. The open access fees (article-processing charges) for this journal are kindly sponsored by ESR for all Members. The journal went open access in 2012, which means that all articles published since then are freely available online.