Artificial intelligence in breast cancer: clinical applications in diagnosis, prognosis, and therapeutics.
Authors
Affiliations (10)
Affiliations (10)
- Thelansis Knowledge Partners, Gurugram, India.
- Department of Pharmaceutics, College of Pharmacy, Jouf University, Sakaka, Saudi Arabia.
- College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.
- Department of Pharmaceutics and Industrial Pharmacy, College of Pharmacy, Taif University, Taif, Saudi Arabia.
- Department of Pharmacology and Toxicology, College of Pharmacy, Umm Al-Qura University, Makkah, Saudi Arabia.
- Department of Pharmacology, College of Pharmacy, King Khalid University, Asir-Abha, Saudi Arabia.
- Department of Pharmaceutical Sciences, Shalom Institute of Health & Allied Sciences, Sam Higginbottom University of Agriculture, Technology & Sciences, Prayagraj, India.
- College of Nursing, KKECS Group of Institutions, Bangalore, India.
- Department of Molecular and Cellular Engineering, Jacob Institute of Biotechnology and Bioengineering, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj, India.
- Department of Pharmaceutics, College of Pharmacy, University of Hafr Al Batin, Al-Batin, Saudi Arabia.
Abstract
Breast cancer (BC) presents a considerable global health challenge and is characterized by increasing mortality and morbidity rates. Prompt screening and accurate diagnosis are crucial for improving patient outcomes. For the assessment of BC, radiographic imaging modalities such as digital breast tomosynthesis (DBT), ultrasound, digital mammography (DM), magnetic resonance imaging (MRI), and nuclear medicine procedures are commonly used. The gold standard for confirming cancer is histopathology. To effectively support the segmentation, diagnosis, and prognosis of BC. Artificial intelligence (AI) technologies show great promise for the quantitative depiction of medical images.This review explores recent strides in AI applications for BC. The literature search from 2018 to 2025 was performed with the PubMed database. It includes rapid breast lesion detection, segmentation, cancer diagnosis and enhanced imaging quality through data augmentation. It also discusses the biological characterization of BC via AI-based classification tools, including subtyping and staging. Furthermore, this review also explores the use of multiomics data to predict clinical outcomes such as survival, treatment response, and metastasis in BC. Additionally, we recognized the challenges faced by AI in BC in real-world applications, including organizing data, model interpretability, and regulatory compliance.