JMIR Publications

ABSTRACT

Background:

Artificial intelligent agents (AIAs), capable of autonomous decision-making without human intervention, are increasingly discussed for future use in health care. However, empirical evidence on how prospective health care professionals (HCPs) evaluate ethically consequential decisions made by AIA compared to human decision-makers remains limited, particularly across different clinical settings and ethical principles.

Objective:

This study examined whether medical students and psychology or psychotherapy students evaluate identical medical moral dilemmas differently depending on whether decisions are made by an AIA or a human decision-maker, and whether these evaluations differ by principle of action (utilitarian vs. deontological) and medical scenario setting (somatic vs. psychiatric).

Methods:

In this randomized, factorial, anonymous web-based vignette study, students from Germany, Austria, and Switzerland rated 12 medical moral dilemma vignettes on acceptance and responsibility attribution (6-point Likert scales), following a full factorial 2 (decision-maker) x 2 (scenario setting) x 2 (principle of action) design. The data were analyzed in R using linear mixed models with Type III Wald tests.

Results:

A total of N=358 participants (M age=25.0 years, SD=7.42; 74.6% female; M semester=5.9, SD=3.5) provided 4296 scenario ratings. Decisions made by AIA were rated as less acceptable (P<.001) and were attributed less responsibility (P=.002) than identical decisions made by humans. Utilitarian decisions were more accepted and attributed more responsibility than deontological decisions (both P<.001). Acceptance was lower for psychiatric than somatic scenarios (P<.001), though this setting effect was not significant for responsibility attribution (P=.22). The acceptance gap between AIA and human decision-makers was larger in somatic than psychiatric settings (P=.02), driven by declining acceptance of human decisions in psychiatric scenarios, while AIA acceptance remained stable across settings. Responsibility attribution showed a three-way interaction between decision-maker, principle of action, and scenario setting (P<.001). In psychiatric scenarios, humans were attributed more responsibility than AIA regardless of principle of action, whereas in somatic scenarios, this gap emerged only for utilitarian decisions. Neither field of study nor other demographic variables were associated with acceptance or responsibility attribution.

Conclusions:

Prospective HCPs consistently rated decisions made by AIAs as less acceptable and less deserving of responsibility than identical human decisions, but this gap varied systematically by clinical setting and ethical principle rather than reflecting a uniform aversion. These findings suggest that acceptance of and responsibility attribution toward AIAs in health care are context-dependent judgments, with implications for the design and clinical integration of autonomous artificial intelligent systems.