Revolutionizing Breast Cancer Detection with Artificial Intelligence and Machine Learning Breakthroughs in Imaging and Diagnosis: Literature Review
Ramazanova Z. Baiken Y. Matkarimov B. Baimagambet Z. Aituov B. Myngbay A. Urazbaev A.
December 2025Engineered Science Publisher
Engineered Science
2025#38
Cancer is a major global health challenge, with 19.3 million new cases and 10 million deaths in 2021. Breast cancer, with 2.3 million cases in 2022, is among the most common. Personalized treatment is vital for better outcomes but requires collaboration between clinicians and researchers. Traditional diagnostic methods, relying on manual histological examination, are slow and error-prone, worsened by a global shortage of pathologists, highlighting the need for more reliable diagnostic tools. The digitization of tissue slides has enabled artificial intelligence (AI) and machine learning (ML) integration into medical imaging, promising improved patient care. This study evaluates AI-based computational models for digital pathology, focusing on breast cancer diagnosis. These models aim to boost diagnostic accuracy and efficiency, overcoming limitations of conventional histopathological methods. We assessed various AI-based models for digital pathology, emphasizing their potential to enhance patient outcomes, treatment planning, and diagnostic accuracy in oncology, particularly for breast cancer. This paper reviews recent AI applications in oncology, highlighting their strengths and current challenges, and underscores their ability to improve the accuracy and efficiency of diagnostic processes.
Artificial intelligence , Breast cancer , Diagnostic accuracy , Digital pathology , Machine learning , Medical imaging , Oncology
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PI National Laboratory Astana, Nazarbayev University, 53 Kabanbay Batyr Avenue, Astana, 010000, Kazakhstan
School of Engineering and Digital Sciences, Nazarbayev University, 53 Kabanbay Batyr Avenue, Astana, 010000, Kazakhstan
Center for Bioenergy Research LLP, 53 Kabanbay Batyr Avenue, Astana, 010000, Kazakhstan
Department of Biology, K. Zhubanov Aktobe Regional University, Aktobe, 030000, Kazakhstan
School of Medicine, Nazarbayev University, 5 Kerey and Zhanibek Khans St, Astana, 010000, Kazakhstan
PI National Laboratory Astana
School of Engineering and Digital Sciences
Center for Bioenergy Research LLP
Department of Biology
School of Medicine
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