Authors
Attila Imre, Bertalan Nemeth, Ákos Józwiak, Balázs Nagy, László Balkányi, Ahmad Fasseeh, Zoltán Kaló, Antal Zemplenyi
Published in
Expert review of pharmacoeconomics & outcomes research. Aug 17, 2026. Epub Aug 17, 2026.
Abstract
Health economic models (HEMs) provide a solid foundation for reimbursement policy decisions that shape patient access to new treatments and the allocation of scarce healthcare resources. Model development is labor-intensive and time-consuming, often requiring months of expert work. Recent advances in large language models (LLMs) prompted interest in whether artificial intelligence can support or partially automate this process, but the evidence base remains scattered and has not been mapped against the modeling workflow.
This review examines current applications of LLMs to health economic modeling. Five proof-of-concept studies are included and mapped to an eight-stage workflow adapted from the ISPOR-SMDM Modeling Good Research Practices framework and discussed in terms of reproducibility, validation, adaptability, and technology readiness. Published work addressed model parameterization, model implementation, reporting and quality assessment, and local adaptation, while research question design, model conceptualization, uncertainty analysis, and model validation remained unaddressed.
The evidence supports cautious optimism. Near-term gains are augmenting human modelers on decomposed, verifiable sub-tasks rather than pursuing autonomous end-to-end modeling, which remains distant given current reliability levels and the iterative, collaborative nature of model development.
PMID:
42605534
Bibliographic data and abstract were imported from PubMed on 17 Aug 2026.
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