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Preferences for AI-Enabled Health Care Technologies: Systematic Review of Discrete Choice Experiments and Reporting
Xinyue Zhang1,2, Shimeng Liu1,2, Yangchen Ji1,2
1School of Public Health, Fudan University, No.130 Dongan Road, Xuhui District, Shanghai, 200032, China, 86 13046006196.
Journal of Medical Internet Research
|August 10, 2026
Summary
This review found that while usability is often studied, effectiveness is key for AI healthcare preferences. Reporting quality is high, but study design details need improvement for better AI technology development.
Area of Science:
- Health Informatics
- Artificial Intelligence in Healthcare
- Behavioral Economics
Background:
- Artificial intelligence (AI) integration in healthcare necessitates understanding stakeholder preferences for AI-enabled technologies.
- Discrete Choice Experiments (DCEs) are common for preference elicitation, but evidence synthesis on attributes, willingness to pay (WTP), and reporting quality is lacking.
Purpose of the Study:
- To systematically review stakeholder preferences for AI-enabled healthcare technologies using DCEs.
- To assess the reporting quality of AI-related DCE studies using the DIRECT checklist.
- To identify key preference attributes and methodological gaps for AI technology development.
Main Methods:
- Systematic review following PRISMA guidelines, searching multiple databases (PubMed, Embase, etc.) up to March 2026.
- Included studies reporting original DCE data on AI-enabled healthcare technologies.
- Extracted and summarized study characteristics, design features, econometric models, attributes, preference outcomes, WTP, and reporting quality (DIRECT checklist).
Main Results:
- 27 studies (28 DCEs) involving patients, clinicians, and the public across various AI applications.
- 163 attributes identified; usability (36.81%) was most frequent, but effectiveness (performance) was most important.
- High average reporting completeness (84.47%), but inconsistencies in reporting design effects (37.04%) and randomization (51.85%).
Conclusions:
- A gap exists between commonly studied attributes and stakeholder priorities in AI healthcare DCEs.
- Effectiveness is a primary driver of preferences, though context-dependent; usability and performance importance vary by application.
- Improved transparency in study design and reporting is needed to enhance DCE evidence interpretability and guide AI development.
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