The role of AI for improved management of breast cancer: Enhanced diagnosis and health disparity mitigation

IF 4.8 2区医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer methods and programs in biomedicine Pub Date : 2025-09-01 DOI:10.1016/j.cmpb.2025.109036

Oluwatunmise Akinniyi , Jose Dixon , Joseph Aina , Francesca Weaks , Gehad A. Saleh , Md Mahmudur Rahman , Timothy Meeker , Hari Trivedi , Judy Wawira Gichoya , Fahmi Khalifa

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引用次数: 0

Abstract

Breast Cancer (BC) remains a leading cause of morbidity and mortality among women globally, accounting for 30% of all new cancer cases (with approximately 44,000 women dying), according to recent American Cancer Society reports. Therefore, accurate BC screening, diagnosis, and classification are crucial for timely interventions and improved patient outcomes. The main goal of this paper is to provide a comprehensive review of the latest advancements in BC detection, focusing on diagnostic BC imaging, Artificial Intelligence (AI) driven analysis, and health disparity considerations. We first examine diverse imaging techniques such as Mammography, Ultrasound, and Dynamic Contrast-Enhanced Magnetic Resonance Imaging, and provide an overview of their pros and cons. Then, we provided an intensive review of the State-of-the-Art (SOTA) literature on the role of AI in BC classification and segmentation. Lastly, we examined the role of AI in BC health disparities. A key contribution of this work lies in its integrative approach, consolidating insights from multiple research areas, imaging methods, AI-driven methodologies, and health disparities in a single resource. This paper evaluates the effectiveness of modern AI-based tools in enhancing diagnostic accuracy and discusses their potential to address biases in BC diagnosis, thus promoting equitable healthcare access. By integrating clinical, technical, and equity perspectives, this review aims to inform real-world decision-making, supporting the development of bias-aware AI tools, guiding equitable screening policy, and enhancing clinical practice in breast cancer care. Additionally, our critical analysis and discussion of recent SOTA highlights the strengths, limitations, and knowledge gaps for future directions of AI roles in BC. In total, these findings and future venue suggestions serve as a practical reference for researchers, clinicians, and policymakers, underscoring the need for interdisciplinary collaboration to harness AI’s full potential in BC diagnosis and reduce global health disparities.

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人工智能在改善乳腺癌管理中的作用：增强诊断和缓解健康差距

根据美国癌症协会最近的报告，乳腺癌（BC）仍然是全球女性发病率和死亡率的主要原因，占所有新癌症病例的30%（约有44,000名女性死亡）。因此，准确的BC筛查、诊断和分类对于及时干预和改善患者预后至关重要。本文的主要目的是全面回顾BC检测的最新进展，重点是诊断BC成像，人工智能（AI）驱动分析和健康差异考虑。我们首先研究了不同的成像技术，如乳房x线摄影、超声和动态对比增强磁共振成像，并概述了它们的优缺点。然后，我们对人工智能在BC分类和分割中的作用进行了深入的回顾。最后，我们研究了人工智能在不列颠哥伦比亚省健康差异中的作用。这项工作的一个关键贡献在于其综合方法，将来自多个研究领域、成像方法、人工智能驱动的方法和健康差异的见解整合到单一资源中。本文评估了现代基于人工智能的工具在提高诊断准确性方面的有效性，并讨论了它们解决BC诊断偏差的潜力，从而促进公平的医疗保健获取。通过整合临床、技术和公平的观点，本综述旨在为现实世界的决策提供信息，支持偏见感知人工智能工具的开发，指导公平的筛查政策，并加强乳腺癌护理的临床实践。此外，我们对最近SOTA的批判性分析和讨论强调了BC中AI角色未来方向的优势、局限性和知识差距。总的来说，这些发现和未来的地点建议为研究人员、临床医生和政策制定者提供了实用参考，强调了跨学科合作的必要性，以充分利用人工智能在BC诊断中的潜力，并缩小全球健康差距。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computer methods and programs in biomedicine 工程技术-工程：生物医学

CiteScore

12.30

自引率

6.60%

发文量

601

审稿时长

135 days

期刊介绍： To encourage the development of formal computing methods, and their application in biomedical research and medical practice, by illustration of fundamental principles in biomedical informatics research; to stimulate basic research into application software design; to report the state of research of biomedical information processing projects; to report new computer methodologies applied in biomedical areas; the eventual distribution of demonstrable software to avoid duplication of effort; to provide a forum for discussion and improvement of existing software; to optimize contact between national organizations and regional user groups by promoting an international exchange of information on formal methods, standards and software in biomedicine. Computer Methods and Programs in Biomedicine covers computing methodology and software systems derived from computing science for implementation in all aspects of biomedical research and medical practice. It is designed to serve: biochemists; biologists; geneticists; immunologists; neuroscientists; pharmacologists; toxicologists; clinicians; epidemiologists; psychiatrists; psychologists; cardiologists; chemists; (radio)physicists; computer scientists; programmers and systems analysts; biomedical, clinical, electrical and other engineers; teachers of medical informatics and users of educational software.