Investigating Whether AI Will Replace Human Physicians and Understanding the Interplay of the Source of Consultation, Health-Related Stigma, and Explanations of Diagnoses on Patients' Evaluations of Medical Consultations: Randomized Factorial Experiment
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View abstract on PubMed
Summary
This summary is machine-generated.Patients trust human physicians more than artificial intelligence (AI) for medical diagnosis, but clear explanations improve AI consultations. AI is not yet a replacement for human doctors in patient care.
Area Of Science
- Human-Computer Interaction
- Medical Artificial Intelligence
- Health Communication
Background
- Artificial intelligence (AI) in medicine offers potential for enhanced accuracy and efficiency in diagnosis and consultation.
- However, empirical evidence is lacking on whether AI improves patient experience at functional, relational, and emotional levels.
- Patient perspective is crucial as AI may not inherently boost confidence, interaction quality, or emotional well-being.
Purpose Of The Study
- To evaluate if AI or human-involved AI can substitute human physicians in diagnosis from a patient-centered viewpoint.
- To assess AI's impact on functional, relational, and emotional aspects of medical consultations.
- To understand how health-related differences in human-AI versus human-human interactions influence patient evaluations.
Main Methods
- A factorial experiment involving 249 participants.
- Investigated three consultation sources (AI, human-involved AI, human) across varying levels of health-related stigma and diagnosis explanation.
- Examined main and interaction effects on functional, relational, and emotional evaluations of the medical consultation.
Main Results
- Patients demonstrated higher trust in human physician diagnoses compared to AI or human-involved AI.
- No significant differences were found in relational and emotional evaluations between human-AI and human-human interactions.
- Diagnosis explanations significantly improved functional, relational, and emotional evaluations across all consultation types.
Conclusions
- Current AI technology is not trusted as much as human expertise for medical diagnosis, aligning with algorithm aversion theory.
- Patient privacy concerns for stigmatized diseases did not lead to a preference for AI over humans; diagnostic needs were paramount.
- Effective diagnosis explanations enhance patient-AI interactions, improving adherence, relationships, and emotional outcomes, offering design insights for medical AI.
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