Applications of Artificial Intelligence and the Challenges in Health Technology Assessment: A Scoping Review and Framework With a Focus on Economic Dimensions Publisher



M Ramezani MARYAM ; A Bakhtiari AHAD ; R Daroudi RAJABALI ; Mr Mobinizadeh Mohammadreza REZA ; Aa Fazaeli Ali AKBAR ; A Olyaee Manesh ALIREZA ; Hr Rabiee Hamidreza REZA ; H Mostafavi HAKIMEH ; S Sazgarnejad SAHARNAZ ; S Bordbar SANAZ
Source: Health Economics Review Published:2025

Abstract

Background: Health Technology Assessment (HTA) is a crucial tool for evaluating the worth and roles of health technologies, and providing evidence-based guidance for their adoption and use. Artificial intelligence (AI) can enhance HTA processes by improving data collection, analysis, and decision-making. This study aims to explore the opportunities and challenges of utilizing artificial intelligence (AI) in health technology assessment (HTA), with a specific focus on economic dimensions. By leveraging AI’s capabilities, this research examines how innovative tools and methods can optimize economic evaluation frameworks and enhance decision-making processes within the HTA context. Methods: This study adopted Arksey and O’Malley’s scoping review framework and conducted a systematic search in PubMed, Scopus, and Web of Science databases. It examined the benefits and challenges of AI integration into HTA, with a focus on economic dimensions. Findings: AI significantly enhances HTA outcomes by driving methodological advancements, improving utility, and fostering healthcare innovation. It enables comprehensive assessments through robust data systems and databases. However, ethical considerations such as biases, transparency, and accountability emphasize the need for deliberate planning and policymaking to ensure responsible integration within the HTA framework. Conclusion: AI applications in HTA have significant potential to enhance health outcomes and decision-making processes. However, the development of robust data management strategies and regulatory frameworks is essential to ensure effective and ethical implementation. Future research should prioritize the establishment of comprehensive frameworks for AI integration, fostering collaboration among stakeholders, and improving data quality and accessibility on an ongoing basis. © 2025 Elsevier B.V., All rights reserved.