Artificial Intelligence in Oncology for Early Detection and Intervention: Clinical and Operational Insights from Malaysia

Abstract

Background: Cancer is a significant public health challenge in Malaysia, with breast, lung, and colorectal cancers as major contributors to morbidity and mortality. Early detection and timely intervention are essential to improving survival rates, yet conventional oncology services often face delays and resource contraints. Artificial Intelligence (AI) has emerged as a transformative tool capable of enhancing diagnostic accuracy, treatment planning, and patient monitoring. Methods: A scoping review was conducted using the PRISMA-ScR framework. Articles published between 2015 and 2025 were retrieved from PubMed, Google Scholar, and IEEE Xplore. Studies were screened and selected based on predefined inclusion and exclusion criteria, before being analysed into three clinical domains: diagnosis and screening, treatment planning, and monitoring and prognosis. Results: Fifteen studies were included. AI applications demonstrated improved diagnostic sensitivity and specificity, radiotherapy planning, and accurate survival prediction models. Operationally, AI contributed to enhanced workflow efficiency, cost reductions, and better decision-making support, particularly in resource-limited settings. Conclusion: AI shows significant promise for advancing early detection and intervention in Malaysian oncology, delivering both clinical and operational benefits. Addressing infrastructure gaps, standardising data, and resolving ethical challenges are critical to achieve full integration of AI into national cancer care strategies.