Cost-Effectiveness of AI in Breast Cancer Screening and Lung Cancer Diagnostics: A Case Study From Costa Rica.

OBJECTIVES: This study evaluated the cost-effectiveness of integrating artificial intelligence (AI) into breast and lung cancer diagnostic workflows in Costa Rica, addressing the scarcity of health economic evidence for AI adoption in low- and middle-income countries and focusing on context-specific systemic barriers. METHODS: Two state-transition Markov models compared AI-assisted workflows (AI as a first reader) with conventional radiologist-only strategies over a 10-year horizon. AI-supported mammography screening was evaluated in asymptomatic women aged 50 to 70 years. For lung cancer, symptom-driven pathways were modeled using GLOBOCAN 2022 incidence data. Inputs included region-specific costs (USD), quality-adjusted life-years (QALYs), and progression probabilities, calibrated to Costa Rica's cancer registry. Methodological rigor adhered to the Consolidated Health Economic Evaluation Reporting Standards 2022 guidelines. RESULTS: AI in breast cancer screening demonstrated economic dominance: it improved QALYs by 49% (10.46 vs 7.04) and reduced costs by 49% (USD 93 102 vs USD 183 278), yielding a net monetary benefit of USD 212 119 at Costa Rica's willingness-to-pay threshold. For lung cancer, the AI strategy also proved dominant: it reduced total costs to USD 1 218 507 (vs USD 1 337 640) while increasing QALYs to 6.88 (vs 4.15), driven by earlier detection that averts expensive late-stage palliative care. CONCLUSIONS: AI significantly improved cost-effectiveness in both breast and lung cancer diagnostics, demonstrating economic dominance by mitigating diagnostic delays and reducing late-stage treatment burdens. These findings stress AI's context-dependent impact in low- and middle-income countries and the necessity of early detection investments and equity-focused implementation to align innovation with healthcare system realities.