From general-purpose models to purpose-built artificial intelligence systems for health economic modeling.

Abstract

INTRODUCTION 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. AREAS COVERED 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. EXPERT COMMENTARY 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.