JMIR Publications

ABSTRACT

Background:

Information systems based on artificial intelligence (AI) increasingly spur controversies among medical professionals as they start to outperform medical experts in tasks that previously required complex human reasoning. Prior research in other contexts has shown that such a technological disruption can result in professional identity threats and provoke negative attitudes and resistance to using technology. However, little is known about how AI systems evoke professional identity threats in medical professionals and under which conditions they actually provoke negative attitudes and resistance.

Objective:

This paper investigates how medical professionals’ resistance towards AI can be understood as a result of professional identity threats and temporal perceptions of AI systems. It examines two dimensions of medical professional identity threat: threats to physicians’ expert status (professional recognition) and threats to physicians’ role as an autonomous care provider (professional capabilities). The paper assesses whether those professional identity threats predict resistance to AI systems and change in importance under conditions of varying professional experience and of varying perceived temporal relevance of AI systems.

Methods:

We conducted two online surveys with 164 medical students and 42 experienced physicians across different specializations. The participants were provided with a vignette of a general medical AI system. We measured the experienced identity threats, resistance attitudes and perceived temporal distance of AI. In a subsample, we collected additional data on the perceived identity enhancement to gain a better understanding of how the participants perceived the upcoming technological change beyond mere threat. Qualitative data were coded in a content analysis. Quantitative data were analyzed in regression analyses.

Results:

Both threats to professional recognition and threats to professional capabilities contributed to perceived self-threat and resistance to AI. Self-threat was negatively associated with resistance. Threats to professional capabilities directly affected resistance to AI while the effect of threats to professional recognition was fully mediated through self-threat. Medical students experienced stronger identity threats and resistance to AI compared to medical professionals. Temporal distance of AI changed the importance of professional identity threats. If AI systems were perceived as relevant only in the distant future, the effect of threats to professional capabilities was weaker whereas the effect of threats to professional recognition was stronger. The effect of threats remained robust after including perceived identity enhancement. The results show that distinct dimensions of medical professional identity are affected by the upcoming technological change through AI.

Conclusions:

Our findings demonstrate that AI systems can be perceived as threatening to medical professional identity. Both threats to professional recognition and professional capabilities contribute to resistance attitudes to AI and need to be considered in the implementation of AI systems in clinical practice.