Adopting AI in medical ethics review: A configurational fsQCA study of practitioners' willingness

The rapid integration of AI in medical research necessitates that Research Ethics Committees (RECs) employ AI tools to oversee AI-driven studies. However, systematic research into the willingness of ethics review practitioners to adopt AI remains scarce. This study addresses this gap by investigating the determinants of AI adoption intentions among REC members and staff.

Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Technology-Organization-Environment (TOE) frameworks, a context-adapted questionnaire was developed and distributed to 697 ethics review practitioners across 11 provinces in China. Yielding 516 valid responses. Fuzzy-set Qualitative Comparative Analysis (fsQCA) was applied to identify multiple configurational pathways to high AI adoption willingness and examine differences between the two practitioner groups.

The study established a theoretical framework for AI adoption willingness, integrating performance expectancy, perceived risk, social influence, facilitating conditions, technology anxiety, personal innovativeness. No single necessary condition for high adoption intention was identified, with all consistency scores falling below 0.9. For ethics committee members, seven pathways emerged across three models: social influence-driven, personal innovativeness-driven, and organization-technology synergy-driven (solution coverage=0.744). For ethics committee staff, eight pathways were identified, which were categorized into personal innovativeness-driven and organization-individual innovativeness synergy-driven (solution coverage = 0.753). Performance expectancy and personal innovativeness were core conditions in most pathways, their roles differed markedly between groups.

This study constructs a theoretical model to examine AI adoption willingness among ethics review practitioners. Findings reveal that high adoption readiness emerges from multiple equifinal configurations rather than isolated factors. Ethics committee members require organizational empowerment and social influence, while staff motivation depends primarily on personal innovativeness. These divergent pathways provide suggest distinct implementation strategies: providing institutional safeguards for members while fostering individual innovativeness among staff. These insights will enhance AI-assisted ethics review and inform technology governance in similar healthcare contexts.