Development of reference case for economic evaluation on clinical artificial intelligence for health insurance reimbursement in Thailand
End : 15 July 2025
Development of reference case for economic evaluation on clinical artificial intelligence for health insurance reimbursement in Thailand
About Project
The development of artificial intelligence (AI) in healthcare is advancing rapidly and playing an important role in healthcare worldwide. However, real-world implementation remains challenging in terms of economic value, patient data security, and acceptance by health professionals. Hence, this study was conducted with three main components: (1) a systematic review and meta-analysis of global studies evaluating the cost-effectiveness of precision medical AI (AI-PM); (2) a nationwide online survey of Thai health providers on their use of and perceptions toward AI in healthcare; and (3) a model-based (decision-tree and Markov model) cost-utility analysis of community-based screening for pulmonary tuberculosis (TB) in high-risk populations using AI compared with the current approach.
Results: (1) The literature indicates that, overall, AI-PM tends to be cost-effective or cost-saving, with positive net monetary benefit (NMB) and gains in quality-adjusted life years (QALYs). Key drivers of NMB include the unit cost of AI-PM and the extent to which AI-informed decisions are adhered to in practice. (2) The Thai health-professional survey revealed highly positive views toward using AI to support clinical decision-making and diagnosis, alongside significant concerns about patient data security and the quality of physician–patient communication. (3) The economic evaluation found that AI-based pulmonary TB screening is cost-effective in Thailand at a willingness-to-pay threshold of 160,000 THB per QALY. In particular, using medical AI alone to interpret chest radiographs (AI alone) was the most cost-effective option: although it incurs higher lifetime costs, it delivers better health outcomes than interpretation by general practitioners alone (GP alone).
Conclusion: AI in healthcare has substantial potential to improve healthcare. To enable effective and sustainable adoption, however, standardized approaches to cost-effectiveness evaluation are needed, along with closing reporting gaps, addressing ethical issues such as data security, and establishing robust policy frameworks for implementation and reimbursement within health insurance systems.
Research Team
Related Organizations/
Supporting Organizations
