Challenges of using AI-based synthetic data in health economics and outcomes research

The growing demand for artificial intelligence (AI)-generated synthetic data (SD) in health economics and outcomes research (HEOR) offers both opportunities and risks. As SD might be used in the near future to inform drug pricing, reimbursement, and policy decisions, a rigorous evaluation of its associated challenges is essential.

This non-systematic narrative review provides a conceptual overview of the generation and application of SD in HEOR, based on targeted searches of PubMed and Google Scholar with priority given to the publications from 2019 onwards. We then identified four interconnected challenges: bias as a foundational upstream driver, the privacy-utility trade-off, the absence of standardized human-in-the-loop evaluation, and underdeveloped regulatory and governance frameworks.

The use of SD might offer opportunities to improve data accessibility; however, its adoption as standalone evidence in healthcare decisions is constrained by the absence of HEOR-specific validation standards, equity-centered evaluation metrics, and regulatory guidance. A structured hybrid ecosystem that integrates SD with real-world evidence, supported by coordinated regulatory frameworks and equity impact assessments, will be the most responsible pathway toward meaningful adoption in HEOR.