Public perceptions of AI-driven decision-making in healthcare: A structural equation modeling approach

2026-07-21

AI Summary

This study investigates public perceptions of medical artificial intelligence (AI) decision-making, focusing on perceived usefulness, risk, and fairness. Drawing on cross-sectional survey data from 3,915 respondents, the authors employ structural equation modeling (SEM) to examine the influence of multidimensional latent constructs—including AI literacy, confidence in physicians’ ability to identify AI-generated content, and health information-seeking behaviors. The research presents the first systematic integration of AI literacy, human–AI content discernment confidence, and information source practices, revealing that public trust in medical AI stems primarily from reliance on healthcare professionals rather than the technology itself. Findings indicate that conversational agents enhance perceived usefulness and reduce risk perception, while confidence in physician oversight emerges as the strongest predictor of fairness perceptions, underscoring the pivotal role of human supervision in fostering acceptance of AI in healthcare.

Abstract

Artificial intelligence (AI) is increasingly integrated into healthcare to support diagnostics, decision-making, and administrative processes. However, the successful implementation of AI depends not only on technical performance but also on public perceptions of its helpfulness, riskiness, and fairness. This study examines public perceptions of automated decision-making (ADM) in healthcare. Data were drawn from the first wave of an ongoing longitudinal survey panel. The final sample consisted of 3,915 respondents and was analyzed with structural equation modeling. Perceptions of ADM in healthcare as helpful, risky, and fair were treated as the dependent variables. AI literacy, familiarity with different forms of AI, confidence in clinicians' ability to distinguish AI- from human-generated content, use of conversational agents for health information, and use of traditional digital health information sources were included as exogenous. Greater familiarity with different forms of AI, higher confidence in the clinician's ability to recognize AI-generated content, and use of conversational agents for health information were associated with greater perceived helpfulness. Use of conversational agents was associated with lower perceived risk, whereas greater familiarity with AI and greater reliance on traditional health information sources were associated with higher perceived risk. Perceptions of ADM as fair were most strongly predicted by confidence in the clinician's ability, with additional small positive associations with AI familiarity, AI literacy, and use of conversational agents. Public perceptions of ADM in healthcare are shaped by technological familiarity, use of conversational agents, and confidence in human oversight. Overall, ADM's perceived helpfulness and fairness are driven more by trust in healthcare professionals than by trust in the technology itself.

Problem

• public perception
• AI-driven decision-making
• healthcare
• automated decision-making
• trust

Innovation

• structural equation modeling
• public perception
• AI-driven decision-making
• conversational agents
• human oversight