COST-EFFECTIVENESS OF AI-BASED INTERVENTIONS IN OLDER ADULT CARE: A SYSTEMATIC REVIEW
Author(s)
Qi Gao, MA1, Minji Hong, PhD1, Yot Teerawattananon, PhD, MD2, YI WANG, PhD1.
1Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore, 2Health Intervention & Technology Assessment, Nouthaburi, Thailand.
1Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore, 2Health Intervention & Technology Assessment, Nouthaburi, Thailand.
OBJECTIVES: To better support economic evaluation and prioritization of AI technologies in elder care, this study synthesises the current evidence base on the cost-effectiveness and broader economic impact of AI-enabled interventions.
METHODS: A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PubMed, Embase, Web of Science and EconLit were systematically searched for studies published up to October 2025. Eligible studies were full economic evaluations examining artificial intelligence (AI)-based technologies used in elderly healthcare. Titles, abstracts, and full texts were screened independently by two reviewers. Reporting quality was assessed using the CHEQUE quality assessment tool. Included studies were synthesised using descriptive statistics and narrative synthesis. The analysis was structured according to the four-step ICOPE care pathway.
RESULTS: In total, 30 studies published between 2018 and 2025 were included, covering 14 countries. Most evaluations focused on screening and diagnostic applications of AI (n = 24), particularly in cancer and ophthalmologic conditions. Across studies, model-based approaches predominated, with decision trees, Markov models, and discrete-event simulations commonly used. AI-related costs varied widely across studies, with only a minority drawing on trial-based data. Many evaluations (63.3%, n=19) relied on assumptions, manufacturer quotes, or expert opinion, and cost components were often insufficiently detailed. Most AI interventions (90%, n=27) were found to be cost-effective, with half (n=15) demonstrating cost savings driven by improved diagnostic accuracy, earlier detection, and labour substitution. Key drivers of cost-effectiveness included AI performance parameters, AI-related costs, population characteristics, disease burden, and local healthcare system features.
CONCLUSIONS: This systematic review provides a comprehensive synthesis of economic evidence on AI-assisted technologies in elderly healthcare following the ICOPE care pathway. Findings show that formal evaluation evidence is largely missing for personalized planning and monitoring interventions, with the majority of evaluations focused on screening and diagnostic technologies.
METHODS: A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PubMed, Embase, Web of Science and EconLit were systematically searched for studies published up to October 2025. Eligible studies were full economic evaluations examining artificial intelligence (AI)-based technologies used in elderly healthcare. Titles, abstracts, and full texts were screened independently by two reviewers. Reporting quality was assessed using the CHEQUE quality assessment tool. Included studies were synthesised using descriptive statistics and narrative synthesis. The analysis was structured according to the four-step ICOPE care pathway.
RESULTS: In total, 30 studies published between 2018 and 2025 were included, covering 14 countries. Most evaluations focused on screening and diagnostic applications of AI (n = 24), particularly in cancer and ophthalmologic conditions. Across studies, model-based approaches predominated, with decision trees, Markov models, and discrete-event simulations commonly used. AI-related costs varied widely across studies, with only a minority drawing on trial-based data. Many evaluations (63.3%, n=19) relied on assumptions, manufacturer quotes, or expert opinion, and cost components were often insufficiently detailed. Most AI interventions (90%, n=27) were found to be cost-effective, with half (n=15) demonstrating cost savings driven by improved diagnostic accuracy, earlier detection, and labour substitution. Key drivers of cost-effectiveness included AI performance parameters, AI-related costs, population characteristics, disease burden, and local healthcare system features.
CONCLUSIONS: This systematic review provides a comprehensive synthesis of economic evidence on AI-assisted technologies in elderly healthcare following the ICOPE care pathway. Findings show that formal evaluation evidence is largely missing for personalized planning and monitoring interventions, with the majority of evaluations focused on screening and diagnostic technologies.
Conference/Value in Health Info
2026-09, ISPOR Asia Pacific 2026, Bangkok, Thailand
Value in Health, Volume 55, Issue S1
Code
EE86
Topic
Economic Evaluation
Disease
SDC: Geriatrics