• Synthetic data from generative AI preserves privacy in healthcare modeling. • Generative AI adapts dynamically, surpassing static traditional CEA models. • Enhanced scenario simulations by generative AI aid robust decision-making. • Generative AI integrates real-world evidence, refining predictive accuracy. • Non-linear modeling in AI captures complex healthcare cost-outcome relations. Healthcare economic evaluation increasingly relies on predictive modeling to inform resource allocation decisions. Traditional cost-effectiveness analysis (CEA) methodologies face significant challenges when processing complex, heterogeneous healthcare datasets and accommodating dynamic system variables. This review examines how generative artificial intelligence technologies may transform predictive modeling frameworks in healthcare economics, specifically focusing on potential improvements in accuracy, adaptability, and efficiency in cost-effectiveness analyses. A literature search was conducted across PubMed, Scopus, Web of Science, and IEEE Xplore between October 2024 and January 2025, examining publications from 2018-2024. Critically, we identified a near absence of empirical studies that directly apply and validate generative AI technologies within formal health economic modeling or health technology assessment contexts. Most identified literature addresses general AI/ML applications in healthcare or synthetic data generation in adjacent domains, rather than demonstrating validated use in cost-effectiveness analysis. Generative AI demonstrates promising theoretical capabilities in handling non-linear healthcare relationships, generating privacy-preserving synthetic datasets, and enabling dynamic scenario exploration based on performance in related fields. However, direct empirical evidence comparing generative AI to traditional CEA approaches in real-world health technology assessment remains virtually non-existent. Potential advantages include automated model support, enhanced integration of real-world evidence, and improved handling of missing data scenarios. Technologies such as Generative Adversarial Networks and Variational Autoencoders show early-stage promise in addressing traditional modeling limitations in adjacent applications. Generative AI represents a conceptually significant potential advancement in healthcare economic modeling. However, claims presented are predominantly forward-looking and conceptual rather than empirically validated. Implementation challenges including model interpretability, regulatory frameworks, validation requirements, and ethical considerations require substantial empirical research before successful integration into healthcare decision-making processes.
David-Olawade et al. (Sun,) studied this question.