Journal of Progressive Medicine and Health Care

Abstract

Artificial intelligence (AI) is increasingly being used to support population-health activities such as risk identification, prevention, monitoring, and intervention planning. However, identifying a health risk or an intervention opportunity does not necessarily mean that people will understand, trust, accept, or engage with the resulting intervention. This conceptual and methodological paper proposes Mind Genomics as a framework for studying how different people respond to AI-enabled population-health interventions. The framework organizes potential intervention features into four conceptual domains: Trust and Transparency, Convenience and Delivery, Social and Contextual Fit, and Autonomy and Human Support, comprising 16 proposed elements. In a future Mind Genomics study, these elements could be incorporated into BimiLeap, a web-based platform for designing, conducting, and analyzing Mind Genomics studies. BimiLeap would combine the proposed elements to generate experimentally designed vignettes, which respondents would evaluate according to their willingness to engage with the proposed population-health programme. BimiLeap would then process the resulting ratings through its integrated Mind Genomics analytical workflow to identify the relative contribution of intervention elements and potential response-based mindsets. These findings could help identify which intervention features are more strongly associated with engagement among different groups of people and could support the development of more appropriately tailored communication or intervention strategies. Future empirically identified mindsets could also provide a basis for developing a Personal Viewpoint Identifier (PVI) to support more personalized communication. A hypothetical AI-enabled diabetes prevention and management programme is used to illustrate how the framework could be applied, with potential applications across other population-health contexts. Overall, the paper presents a conceptual pathway linking population-health needs, AI-supported intervention opportunities, Mind Genomics, and human engagement, with future empirical research needed to determine its practical value.