The value for money of artificial intelligence-empowered precision medicine : a systematic review and regression analysis

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Zhang, Yue and Lin, Ziwei and Teerawattananon, Yot and Akksilp, Katika and Morton, Alec and Wang, Yi and Prapinvanich, Thittaya and Dulsamphan, Thamonwan and Chen, Wenjia (2026) The value for money of artificial intelligence-empowered precision medicine : a systematic review and regression analysis. npj Digital Medicine, 9 (1). 78. ISSN 2398-6352 (https://doi.org/10.1038/s41746-025-02259-w)

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Abstract

Artificial intelligence has empowered precision medicine (AI-PM) to transform healthcare. This study synthesized available evidence on the cost-effectiveness of AI-PM. We systematically searched five major databases for economic evaluations of AI-PM, extracted data, and assessed risk-of-bias using the Bias in Economic Evaluation (ECOBIAS) checklist. For cost-utility analyses, the value-for-money was quantitatively summarized, and regression analyses incorporating machine learning were conducted to explore value heterogeneity. Forty-eight economic evaluations were included, of which 31 were cost-utility analyses. Although risk-of-bias assessment indicated potential systematic optimism, AI-PM was cost-saving or cost-effective in 89% of base-case analyses, with incremental cost-effectiveness ratios ranging from dominant to $129,174/quality-adjusted life-year (QALY). Interquartile ranges of incremental costs (-$259 to $28), QALY gains (0.001-0.019), and net monetary benefits (NMB; $18 to $986 at a willingness-to-pay threshold equal to one-time per-capita GDP) indicated modest health gains at minimal additional costs, and likely high value heterogeneity. Modeling choices and system-level factors were identified as essential sources of heterogeneity in estimated NMBs. Additional value assessment revealed low adaptability and underreported key value factors, leaving significant uncertainties in AI-PM adoption. [Abstract copyright: © 2025. The Author(s).]

ORCID iDs

Zhang, Yue, Lin, Ziwei, Teerawattananon, Yot, Akksilp, Katika, Morton, Alec ORCID logoORCID: https://orcid.org/0000-0003-3803-8517, Wang, Yi, Prapinvanich, Thittaya, Dulsamphan, Thamonwan and Chen, Wenjia;
CORE (COnnecting REpositories)