aceCost-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 screen…
In Markov models for Costa Rica, using AI as a first reader in breast screening and lung cancer diagnosis was projected to lower costs while increasing quality-adjusted life-years over 10 years.
The findings come from simulation models over a 10-year horizon, with the authors noting AI's impact is context-dependent and tied to system barriers and implementation realities, so results are not measured outcomes from actual deployment.
Evidence
- Peer-reviewedValue in Health Regional Issues2026-10-01
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Truvace Impact Record TRV-2026-1441, v1: “Cost-Effectiveness of AI in Breast Cancer Screening and Lung Cancer Diagnostics: A Case Study From Costa Rica.” Truvace, 2026-10-15. /record/TRV-2026-1441 (accessed at citation time). sha256 534249dea214fd2b…
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