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<p>AI has revolutionized breast cancer care. It enhances diagnostic accuracy, improves early detection, and personalizes treatment while reducing unnecessary testing. Thus, it is crucial to examine the incorporation of AI to emphasize its potential benefits for diagnostic precision, screening optimization, and overall patient outcomes within clinical workflows. Utilizing bibliometric analysis, this research identifies significant contributions and landmark studies that have driven advancements in AI applications for breast cancer. This analysis further highlights trending themes and technologies, which are vital for guiding future research initiatives and aligning resource allocation with the most urgent challenges in the field. The research analyses ninety-seven articles concerning AI applications in the detection and treatment of breast cancer, which are indexed in the Web of Science, for the purpose of identifying themes and conducting analysis. The initial section presents a comprehensive overview of research growth, leading authors, key publishers and journals contributing to the literature. Subsequently, an analysis of major studies related to AI in the detection and treatment of breast cancer have been provided. There has been a marked upward trend in research output on AI applications in breast cancer in 2023 following a gradual rise over the past 25 years. Future research directions in AI for breast cancer should emphasize enhancing algorithm accuracy, integrating multi-modal imaging data, and improving real-time analytical capabilities. Additionally, further exploration of AI’s role in personalized treatment plans and patient monitoring could substantially improve clinical outcomes and optimize healthcare resources.</p>

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A review of application of Artificial Intelligence in breast cancer detection and treatment

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月19日

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  • Aaliyah Siddiqui,
  • Krunal Khobragade,
  • Pradeep Kautish,
  • Mujahid Siddiqui,
  • Zericho Marak

摘要

AI has revolutionized breast cancer care. It enhances diagnostic accuracy, improves early detection, and personalizes treatment while reducing unnecessary testing. Thus, it is crucial to examine the incorporation of AI to emphasize its potential benefits for diagnostic precision, screening optimization, and overall patient outcomes within clinical workflows. Utilizing bibliometric analysis, this research identifies significant contributions and landmark studies that have driven advancements in AI applications for breast cancer. This analysis further highlights trending themes and technologies, which are vital for guiding future research initiatives and aligning resource allocation with the most urgent challenges in the field. The research analyses ninety-seven articles concerning AI applications in the detection and treatment of breast cancer, which are indexed in the Web of Science, for the purpose of identifying themes and conducting analysis. The initial section presents a comprehensive overview of research growth, leading authors, key publishers and journals contributing to the literature. Subsequently, an analysis of major studies related to AI in the detection and treatment of breast cancer have been provided. There has been a marked upward trend in research output on AI applications in breast cancer in 2023 following a gradual rise over the past 25 years. Future research directions in AI for breast cancer should emphasize enhancing algorithm accuracy, integrating multi-modal imaging data, and improving real-time analytical capabilities. Additionally, further exploration of AI’s role in personalized treatment plans and patient monitoring could substantially improve clinical outcomes and optimize healthcare resources.