ARTIFICIAL INTELLIGENCE IN EARLY CANCER DETECTION: A PARADIGM SHIFT IN ONCOLOGY DIAGNOSTIC

Abstract

Early detection of cancer is a critical factor in improving survival rates and treatment outcomes. In Pakistan, where diagnostic delays, inadequate screening infrastructure, and shortage of specialized personnel are prevalent, Artificial Intelligence (AI) offers a transformative opportunity for early oncology diagnostics. This study assesses the diagnostic performance and contextual feasibility of AI-based models—particularly machine learning (ML) and deep learning (DL)—in identifying early-stage cancers using imaging modalities such as mammography, CT scans, MRI, and histopathology. A mixed-methods approach was employed, involving retrospective analysis of imaging datasets collected from three leading institutions: Shaukat Khanum Memorial Cancer Hospital & Research Centre (Lahore), Aga Khan University Hospital (Karachi), and Pakistan Institute of Medical Sciences (PIMS, Islamabad). AI models including convolutional neural networks (CNNs) and decision tree classifiers were trained and tested on over 1200 anonymized and annotated imaging samples. Performance was evaluated using key metrics such as sensitivity, specificity, accuracy, and area under the curve (AUC). Additionally, semi-structured interviews with radiologists, oncologists, and medical IT staff at the selected hospitals were conducted to explore infrastructural readiness, ethical considerations, and clinical acceptability of AI integration. Findings revealed that AI-powered diagnostic tools achieved high sensitivity and accuracy in detecting early-stage malignancies, often improving diagnostic speed and reducing observer bias. However, infrastructural disparities, inconsistent digitization of patient records, and the need for physician training were identified as key barriers to implementation. Despite these challenges, healthcare professionals expressed cautious optimism about integrating AI to support Pakistan’s overburdened healthcare system.The study concludes that AI holds significant promise for enhancing early cancer detection in Pakistan. It emphasizes the importance of strategic investments in digital infrastructure, training, and policy frameworks to enable safe, effective, and equitable adoption of AI in oncology.