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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 screen…

By the Truvace desk2 min read1 sourcePeer-reviewedTRV-2026-1441· cite & verify
Cost-Effectiveness of AI in Breast Cancer Screening and Lung Cancer Diagnostics: A Case Study From Costa Rica
Adenocarcinoma - CT scan (5499628365) by Yale Rosen from USA. CC BY-SA 2.0 · https://creativecommons.org/licenses/by-sa/2.0

In brief

Researchers built two state-transition Markov models to compare AI-assisted diagnostic workflows, with AI acting as first reader, against radiologist-only strategies in Costa Rica over a 10-year horizon. One model covered mammography screening for asymptomatic women aged 50 to 70, and the other covered symptom-driven lung cancer pathways.

In both models the AI strategy came out cheaper and produced more QALYs, which the authors describe as economic dominance. The result matters for low- and middle-income countries, where health economic evidence on AI is scarce. These are modelled projections rather than observed outcomes from deployed systems, so real-world results will depend on implementation and system conditions.

Main points

  1. Breast screening model: QALYs rose 49% (10.46 vs 7.04) while costs fell 49% (USD 93 102 vs USD 183 278).
  2. Net monetary benefit for breast screening was USD 212 119 at Costa Rica's willingness-to-pay threshold.
  3. Lung cancer model: total costs fell to USD 1 218 507 from USD 1 337 640 while QALYs rose to 6.88 from 4.15.
  4. Savings in lung cancer were attributed to earlier detection that averts expensive late-stage palliative care.
  5. Authors call for early detection investment and equity-focused implementation.

The gain

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 rundown

The breast cancer model evaluated AI-supported mammography in asymptomatic women aged 50 to 70. Compared with radiologist-only reading, the AI strategy improved QALYs and reduced costs, making it dominant in the model.

The lung cancer model used symptom-driven pathways based on GLOBOCAN 2022 incidence data. Earlier detection under the AI strategy reduced the need for costly late-stage palliative care.

Model inputs used region-specific costs in USD, QALYs, and progression probabilities calibrated to Costa Rica's cancer registry. Reporting followed the Consolidated Health Economic Evaluation Reporting Standards 2022 guidelines.

Sources

  1. Peer-reviewedValue in Health Regional Issues2026-10-01

The debate