IJLTEMAS International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS) IJLTEMAS 2278-2540 IJLTEMAS 353 10.51583/IJLTEMAS.2026.150900002 Application of Internet of a thing in Health Now and Future Use of Artificial Intelligence (Ai) in the Management of Breast Cancer Ayodeji-Ojo Esther Department of Nursing, Ekiti State University Teaching Hospital Ado Ekiti, Nigeria Nigeria Opeyemi Ojo Phebean Department of Computer, Ekiti State College of Technology Ijero, Nigeria Nigeria Oluwaseun AWE Vincent Department of Computer, Ekiti State College of Technology Ijero, Nigeria Nigeria E Olabiyi. Olugbenga Department of Microbiology, Ekiti State University Teaching Hospital Ado Ekiti, Nigeria Nigeria Idowu Oyerinde Wande Department of Anaesthesia, Ekiti State University Teaching Hospital Ado Ekiti, Nigeria Nigeria 15 9 10 19 09 09 2026 14 09 2026 28 09 2026 Artificial Intelligence MRI Breast Cancer Deep learning (DL) radiomics Diagnosis Abstract

Breast cancer (BC) remains a major etiology of motility for female gender in the world, accounting for more than 2.3 million new fatalities annually. Despite early detection and treatment advancements, significant challenges persist, particularly in personalized management and preventive interventions. Recent technological innovations, such as the integration of artificial intelligence (AI), have shown promise in improving BC diagnosis and management. AI has been successfully applied in mammography, MRI, and advanced imaging techniques to enhance the accuracy and efficiency of BC detection, offering earlier intervention and personalized care. AI's ability to analyze high-resolution images has proven effective in detecting breast masses, classifying benign and malignant tumors, and assessing breast cancer risk. Additionally, deep learning (DL) algorithms and radiomics have emerged as valuable tools, providing refined predictive analytics and reducing false-positive rates. However, the widespread adoption of AI in clinical settings is hindered by factors like data heterogeneity, confidentiality concerns, limited generalizability, and the need for specialized training among healthcare professionals. Despite these limitations, AI’s potential in revolutionizing breast cancer care is immense, providing appreciable development in the are of disease screening accuracy and treatment outcomes. Addressing the existing challenges through collaborative research, standardized data, and ethical frameworks is crucial to realizing the full benefits of AI in BC diagnosis and management.

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