AI interventions in cancer screening: balancing equity and cost-effectiveness

This paper examines the integration of artificial intelligence (AI) into cancer screening programmes, focusing on the associated equity challenges and resource allocation implications. While AI technologies promise significant benefits—such as improved diagnostic accuracy, shorter waiting times, reduced reliance on radiographers, and overall productivity gains and cost-effectiveness—current interventions disproportionately favour those already engaged in screening. This neglect of non-attenders, who face the worst cancer outcomes, exacerbates existing health disparities and undermines the core objectives of screening programmes.

Using breast cancer screening as a case study, we argue that AI interventions must not only improve health outcomes and demonstrate cost-effectiveness but also address inequities by prioritising non-attenders. To this end, we advocate for the design and implementation of cost-saving AI interventions. Such interventions could enable reinvestment into strategies specifically aimed at increasing engagement among non-attenders, thereby reducing disparities in cancer outcomes. Decision modelling is presented as a practical method to identify and evaluate these cost-saving interventions. Furthermore, the paper calls for greater transparency in decision-making, urging policymakers to explicitly account for the equity implications and opportunity costs associated with AI investments. Only then will they be able to balance the promise of technological innovation with the ethical imperative to improve health outcomes for all, particularly underserved populations. Methods such as distributional cost-effectiveness analysis are recommended to quantify and address disparities, ensuring more equitable healthcare delivery.