Confounders in Predictive Medical Models: The Role of Religion

Abstract

Aim: The aim of this study was to assess the acceptance among natural science specialists of the current official regulatory recommendations to avoid utilizing artificial intelligence (AI) and machine learning (ML) models that could exacerbate social disparities. Materials and Methods: An anonymous online survey was conducted using the Telegram platform, where participants were asked a single question: "Do you consider the inclusion of religious status in AI and ML models justified from the perspective of medical ethics and science?" Respondents were provided with only two response options: "Yes" or "No." This survey was specifically targeted at international groups, focusing primarily on English and Russian-speaking clinicians and scientific researchers. Results: 134 unique individuals participated in the survey. The results revealed that two-thirds of the respondents (87 individuals) agreed that including Religion status as predictor in the ML and AI models is inappropriate. Conclusion: Two-thirds of healthcare practitioners and scientific researchers participating in this survey agree that categorizing individuals within healthcare settings based on their religion is inappropriate. Constructing healthcare predictive models based on confounders like religion is unlikely to aid in identifying or treating any pathology or disease. However, the high conflict potential of this predictor may deepen societal disparities.