AI and Machine Learning in Breast Cancer - Advancing Precision Medicine Through Data-Driven Models

Aug 3, 2026

Artificial Intelligence (AI) and Machine Learning (ML) advancements are transforming precision medicine by enabling more effective breast cancer diagnosis and treatment. Data-driven models improve accuracy in tumor detection, classification, and prognosis compared with traditional workflows. Deep learning and other ML methods extract subtle patterns from imaging, genomic, and clinical data to support more personalized therapeutic decisions. The review summarizes state-of-the-art techniques, implementation challenges such as privacy and interpretability, and future opportunities for reliable clinical integration.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages6

Reading Time15 min

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International Journal of Innovation in Engineering, Vol 4, No 4,(2024), 37-42 International Journal of Innovation in Engineering journal [homepage: www.ijie.ir](homepage: www.ijie.ir) Review Paper AI and Machine Learning in Breast Cancer: Advancing Precision Medicine Through Data-Driven Models Anuradha Reddya1 a Department of Computer Science and Engineering, Malla Reddy Institute of Technology and Science, Hyderabad, India. | A R T I C L E I N F O | A B S T R A C T
Artificial Intelligence (AI) and Machine Learning (ML) advancements have revolutionized precision medicine, offering transformative breast cancer diagnosis and treatment solutions. By leveraging vast datasets, AI-powered models provide enhanced accuracy in tumor detection, classification, and prognosis, surpassing traditional diagnostic methods. Machine learning algorithms, including deep learning networks, uncover intricate patterns within imaging, genomic, and clinical data, enabling personalized treatment strategies. This paper highlights the integration of AI in breast cancer care, discussing state-of-the-art techniques, challenges in clinical implementation, and future opportunities. Through a comparative analysis of data-driven models, we demonstrate their potential to optimize early detection, improve patient outcomes, and support oncologists in decision-making processes. | | --- | --- | | Received: 20 July 2024
Reviewed: 15 September 2024
Revised: 23 October 2024
Accepted: 7 December 2024 | | | Keywords:
Breast Cancer, Machine Learning, Artificial Intelligence, Tumor Detection, Precision Medicine | | 1 Corresponding author: [anuradhareddy.anu@gmail.com](anuradhareddy.anu@gmail.com) 1. Introduction Breast cancer remains one of the most prevalent and life-threatening diseases worldwide, necessitating advancements in early detection and treatment (Dronamraju, 2022) (Reddy et al., 2022) . Traditional diagnostic approaches, while valuable, often face limitations in accuracy, speed, and scalability (Sachdeva et al., 2024) . Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) have opened new avenues in precision medicine, enabling data-driven models to address these challenges (Narayana et al., 2024) . By analyzing complex datasets, AI systems have shown exceptional promise in detecting subtle patterns, identifying high-risk cases, and recommending tailored treatment strategies (Jaagrit et al., 2023) . This integration of AI and ML marks a paradigm shift in oncology, bringing hope for improved patient outcomes and reduced healthcare burden (Hariharan et al., 2024) . Machine learning, a subset of AI, uses algorithms to learn from data, making predictions or decisions without explicit programming (Vidyullatha et al., 2024) (Saraei et al., 2023) (Ghahremani nahr et al., 2021a)(Shahvaroughi Farahani & Esfahani, 2022) (Rahmaty, 2023) (Ghahremani Nahr et al., 2021b) . Techniques like deep learning, support vector machines, and decision trees are increasingly employed in breast cancer research (Ghantasala, Dilip, et al., 2024) (Rao, 2023) . These methods excel in processing vast volumes of data, including medical imaging, genomic profiles, and clinical records (Ghantasala, Hung, et al., 2024) . For example, deep learning-based convolutional neural networks (CNNs) have significantly improved the accuracy of mammogram analysis (Ghantasala et al., 2023). Moreover, ML models provide valuable insights into the molecular mechanisms of breast cancer, aiding the development of personalized therapies tailored to individual patient profiles. The application of AI in breast cancer is not without challenges. Issues like data privacy, model interpretability, and the need for high-quality, diverse datasets hinder widespread adoption (Arora et al., 2024) . Ethical concerns, including bias in AI models and equitable access to these technologies, further complicate their clinical integration (Narayana et al., 2024). Howev

FAQs

How do AI and ML models improve breast cancer diagnosis and prognosis?

They analyze large datasets to enhance accuracy in tumor detection, classification, and prognosis. Deep learning and other ML approaches uncover subtle patterns in imaging, genomic, and clinical data.

Which AI/ML techniques are commonly used in breast cancer research?

Deep learning methods such as convolutional neural networks are widely used, alongside techniques like support vector machines and decision trees. These models process medical imaging and other patient data to support prediction and classification tasks.

What challenges affect clinical adoption of AI in breast cancer care?

Key challenges include data privacy, model interpretability, and the need for high-quality and diverse datasets. Ethical concerns such as bias and equitable access also complicate integration into clinical workflows.

Aug 3, 2026

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