JMIR Publications

ABSTRACT

Background:

The increasing use of AI in medical diagnosis promises benefits such as greater diagnostic accuracy and efficiency. However, there is little evidence to systematically test whether the ideal technological promises translate into an improved medical experience from the patient’s perspective. This perspective is significant because AI as a technological solution does not necessarily improve patient trust in diagnosis and adherence to treatment at the functional level, create meaningful interactions between the medical agent and the patient at the relational level, and evoke positive emotions and decrease pessimism in the patient during the consultation at the emotional level.

Objective:

This study aims to investigate, from a patient-centered perspective, whether AI or human-involved AI can replace the role of human doctors in diagnosis at the functional, relational, and emotional levels. In addition, how will some health-related differences between human-AI and human-human interactions affect the patient’s evaluation of the medical consultation?

Methods:

A 3 (consultation source: AI vs. human-involved AI vs. human) × 2 (health-related stigma: low vs. high) × 2 (diagnosis explanation: without vs. with explanation) factorial experiment was conducted with 249 participants. Main effects and interaction effects of the variables were examined on individuals’ functional, relational, and emotional evaluations of the medical consultation.

Results:

Functionally, people trusted the diagnosis of human doctors (mean 4.78-4.85, SD 0.06-0.07) more than AI (mean 4.34-4.55, SD 0.06-0.07) or human-involved AI (mean 4.39-4.56, SD 0.06-0.07) (P<.001), but at the relational and emotional levels, there was no significant difference between the human-AI and human-human interactions (P>.05). Health-related stigma had no significant effect on how people evaluated the medical consultation or contributed to preferring AI-based systems over humans (P>.05), but providing explanations of the diagnosis significantly improved the functional (P<.001), relational (P<.05), and emotional (P<.05) evaluations of the consultation for all three medical agents.

Conclusions:

The findings imply that at this early stage of AI development, people trust human expertise more than accurate AI, especially for decisions traditionally made by humans such as medical diagnosis. Surprisingly, even for highly stigmatized diseases such as AIDS where we assume anonymity and privacy are preferred in medical consultations, the dehumanization of AI does not contribute significantly to the preference for AI-based medical agents over humans, suggesting that instrumental needs of diagnosis override patient privacy concerns. Furthermore, providing explanations for medical diagnoses effectively improves treatment adherence, strengthens the doctor-patient relationship, and builds positive emotions during the consultation. This provides insights for the design of AI medical agents, which have long been criticized for lacking transparency while making highly consequential decisions. This study concludes by outlining theoretical contributions to research on health communication and human-AI interaction, and discusses implications for the design and application of medical AI.