ARTIFICIAL INTELLIGENCE IN EARLY ONCOLOGY SCREENING: A SYSTEMATIC REVIEW OF ETHICAL IMPLICATIONS, HEALTH EQUITY, AND DIGITAL ACCESS DISPARITIES

Abstract

Background: Artificial intelligence (AI) has rapidly evolved in oncology over the past decade, transforming early cancer screening through advances in imaging analytics, predictive modeling, and data-driven risk stratification [6,9,17]. Early research primarily focused on diagnostic performance and technical efficiency, however, recent literature increasingly emphasizes the ethical, social, and structural consequences of AI integration into healthcare systems [2,6,8]. Objective: This review analyzes the development of AI in early oncology screening between 2018 and 2026, focusing on ethical implications, health equity, and digital access disparities at the intersection of technology and society. Methodology: A structured literature review was conducted using the PubMed database. Peer-reviewed review articles and policy-oriented studies published between 2018 and 2026 were analyzed. Inclusion criteria comprised relevance to AI-based cancer screening, ethical governance, health equity, social determinants of health, and digital inclusion [1–10]. Results: The literature demonstrates a shift from performance-centered AI development toward socially informed frameworks emphasizing fairness, transparency, and governance [1,6,8]. Although AI technologies enhance early detection potential [9,17], consistent evidence highlights risks of algorithmic bias, unequal access to digital infrastructure, and reinforcement of pre-existing health disparities [2,5,10,12,15]. Vulnerabilities are particularly evident among socioeconomically disadvantaged populations and in low- and middle-income countries (LMICs), where infrastructural limitations restrict equitable implementation [2,18]. Conclusion: AI in early oncology screening holds substantial clinical promise, however, without ethically grounded governance and equity-oriented policy frameworks, its deployment risks amplifying structural inequalities. Sustainable integration requires inclusive data practices, digital access equity, and human-centered regulatory models to ensure socially responsible innovation. Technological innovation in oncology cannot be evaluated independently of its social context and systemic determinants.

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