Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·14d ago·5 min read
Public perceptions of AI-driven decision-making in healthcare: A structural equation modeling approach
TL;DR
Public perceptions of AI-driven decision-making in healthcare are influenced more by trust in clinicians than by technology itself. Familiarity with AI and the use of conversational agents enhance perceived helpfulness and fairness but can also raise perceived risks.
✦ Why It Matters
Focus on building trust with patients to improve acceptance of AI in healthcare settings.
Key Takeaways
- Enhance AI literacy among healthcare professionals and patients.
- Utilize conversational agents to improve patient engagement.
- Build trust in clinicians to foster acceptance of AI tools.
- Monitor public perceptions to guide AI implementation strategies.
Full Summary
AI is increasingly used in healthcare for diagnostics and decision-making, but its success hinges on public perceptions of its helpfulness, riskiness, and fairness. This study analyzed data from 3,915 respondents using structural equation modeling to explore these perceptions.
Key findings revealed that greater familiarity with AI and confidence in clinicians' ability to differentiate AI-generated content correlated with higher perceived helpfulness. Interestingly, while using conversational agents for health information lowered perceived risk, increased familiarity with AI and reliance on traditional health sources heightened it.
Perceptions of fairness were primarily influenced by trust in clinicians, with additional positive associations from AI familiarity and literacy. Overall, the study highlights that public trust in healthcare professionals plays a crucial role in shaping perceptions of AI in healthcare.