Top 10 Most Influential Papers in Artificial Intelligence Since 2000

IntroductionArtificial 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.MethodsA 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’.ResultsFrom 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.ConclusionThis 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 statementThis 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 PointsMultiple 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.Graphical

A survey on literature based discovery approaches in biomedical domain.

Artificial intelligence refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. In the present review we chose ten influential papers from the last five years and through Kintsugi, shed the light on recent evolution of artificial intelligence in anesthesiology. A comprehensive search in in Medline, Embase, Web of Science and Scopus databases was conducted. Each author searched the databases independently and created a list of six articles that influenced their clinical practice during this period, with a focus on their area of competence. During a subsequent step, each researcher presented his own list and most cited papers were selected to create the final collection of ten articles. In recent years purely methodological works with a cryptic technology (black-box) represented by the intact and static vessel, translated to a "modern artificial intelligence" in clinical practice and comprehensibility (glass-box). The purposes of this review are to explore the ten most cited papers about artificial intelligence in anesthesiology and to understand how and when it should be integrated in clinical practice.

The aim of this study was to examine the application of artificial intelligence (AI) and generative AI (GAI) in dental education through a comprehensive bibliometric analysis, with the goal of identifying potential future research areas in this field. Data were obtained from the Web of Science and Scopus databases, with no date restrictions. The inclusion criteria encompassed research articles, reviews and proceedings papers focused on the use of AI in dental education. The Bibliometrix R package was used for data analysis. A total of 54 documents were analysed, revealing a significant increase in publications from 2021 to 2024. The Journal of Dental Education was identified as the most prolific source, with the United States leading in terms of contributions. The findings showed a growing interest in AI's potential to transform dental education, with significant contributions from a diverse set of global authors and institutions. Several key themes were identified, including 'large language models', 'chatbots' and 'clinical decision support systems'. The citation analysis highlighted influential papers, including those by Thurzo etal. (2023) and Yüzbaşıoğlu (2021). There is a growing interest in the use of AI and GAI in dental education. However, further research, particularly experimental studies, is essential to fully understand their impact on educational outcomes in dentistry. Key areas for exploration include the application of AI for personalised learning, the integration of chatbots as educational tools to support students, and the use of AI for training and assessing practical skills.

Aim: The use of machine learning has now become widespread in lung cancer. However, the research trend is still unclear. This study aimed to analyze the most influential publications on artificial intelligence (AI) for lung cancer. Material and Method: A comprehensive PubMed and SCImago Journal and Country Rank (SJR) search was performed. The 50 most cited articles were recorded according to the citation numbers, the country and institute of articles, the name and metrics of the publishing journal, the year of publication, and the content of the articles. Results: The citation numbers ranged from 24 to 628. Annual citations per article was between 1.47 and 104.6. The USA was the country with the most publications (n=22) followed by The Netherlands (n=9) and Peoples R China (n=5). The journal and institution that highly contributed to the 50 most cited articles were Radiology (n=5) and Harvard Medical School (n=5), respectively. Conclusion: The importance of deep learning and AI in lung cancer imaging is increasing day by day. In this study, a detailed bibliometric analysis of the literature on AI in lung cancer imaging was performed. In addition, this bibliometric analysis informs researchers about current influential papers in this field, the characteristics of these studies, and potential future trends in the rapidly evolving field of AI in lung cancer screening.

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