JMIR Publications

ABSTRACT

Background:

The integration of artificial intelligence (AI) into healthcare is increasingly shaping clinical practice and decision-making. Beyond technical performance, AI has implications for professional roles, clinical reasoning, and responsibility. Understanding how medical students and clinicians perceive AI, and how they evaluate AI-supported decisions in different clinical contexts, is therefore important for both implementation and medical education.

Objective:

This study aimed to examine (1) how medical students and clinicians perceive the impact of AI on medical practice and professional identity and (2) how clinicians evaluate explainability, trustworthiness, and responsibility in AI-supported clinical decision-making across different contexts.

Methods:

A two-phase quantitative study was conducted. In Phase 1, medical students (n=93) and clinicians (n=65; N=158) completed a cross-sectional online survey assessing expectations regarding AI, perceived importance of AI across medical domains, and perceived professional identity threat. In Phase 2, a separate sample of clinicians (N=68) was randomly assigned to one of three clinical vignettes (Watson, Triage, OncoGuide) and evaluated AI-supported decisions along the dimensions of explainability, trustworthiness, and responsibility.

Results:

Participants reported positive expectations regarding factual-level impacts of AI on medical practice. However, these expectations were not significantly associated with perceived professional identity threat. Medical students reported significantly higher identity threat than clinicians, despite largely similar expectations regarding AI’s factual and social impact. Perceived importance of AI across medical domains was associated with more positive expectations toward AI-supported medical practice, but not with identity threat. In Phase 2, clinicians’ evaluations of AI-supported decisions varied across contexts. Explainability differed significantly between vignette scenarios (P=.007), with lower ratings in the time-pressured triage context, whereas trustworthiness and responsibility showed no significant differences across scenarios. Descriptive analyses indicated that the relative importance of explainability, trustworthiness, and responsibility varied depending on the clinical situation.

Conclusions:

The findings indicate that positive expectations about AI’s performance do not necessarily reduce professional identity concerns and that these concerns are structured by professional position rather than by expectations about technological benefits. This suggests a decoupling between performance-related expectations and identity-related concerns. In addition, clinicians’ evaluations of AI-supported decisions are context-dependent, particularly with regard to explainability, indicating that requirements for AI are shaped by the specific demands of clinical situations. Together, these results suggest that integrating AI into clinical practice requires consideration not only of technical performance, but also of how clinical judgment, responsibility, and decision evaluation are negotiated in different contexts. This may be particularly relevant for implementation strategies and medical education.