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AI interventions in cancer screening: balancing equity and cost-effectiveness
Cristina Roadevin1, Harry Hill2
1School of Medicine, University of Nottingham, Nottingham, England, UK.
Journal of Medical Ethics
|April 28, 2025
Summary
Artificial intelligence (AI) in cancer screening can improve accuracy and efficiency. However, AI must prioritize non-attenders to reduce health disparities and ensure equitable outcomes for all populations.
Area of Science:
- Health Services Research
- Artificial Intelligence
- Public Health
Background:
- Artificial intelligence (AI) offers potential benefits for cancer screening, including enhanced diagnostic accuracy and cost-effectiveness.
- Current AI implementations in screening often neglect non-attenders, widening existing health disparities.
- Underserved populations face worse cancer outcomes due to barriers in accessing screening programs.
Purpose of the Study:
- To analyze the equity challenges and resource allocation implications of integrating AI into cancer screening.
- To advocate for AI interventions that prioritize non-attenders and address health inequities.
- To propose methods for identifying and evaluating cost-saving AI interventions for equitable healthcare delivery.
Main Methods:
- Case study using breast cancer screening programs.
- Advocacy for cost-saving AI intervention design and implementation.
- Utilizing decision modeling to identify and evaluate interventions.
- Recommending distributional cost-effectiveness analysis to quantify disparities.
Main Results:
- AI in screening currently favors existing attendees, exacerbating inequities.
- Prioritizing non-attenders in AI interventions is crucial for reducing disparities.
- Cost-saving AI interventions can fund strategies to increase engagement among non-attenders.
- Transparency in AI investment decisions is needed to account for equity and opportunity costs.
Conclusions:
- AI integration in cancer screening must balance technological advancement with the ethical imperative for equitable health outcomes.
- Policymakers should explicitly consider equity implications and opportunity costs of AI investments.
- Implementing AI with a focus on non-attenders and underserved populations is essential for reducing cancer outcome disparities.
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