Response scale for each statement:
1 — Strongly disagree
2 — Disagree
3 — Neither agree nor disagree
4 — Agree
5 — Strongly agree
Current AI tools incorporated into clinical workflows have demonstrated sufficient evidence of generalizable performance across diverse patient populations.
Healthcare organizations should prioritize developing internal expertise to validate and monitor AI systems rather than relying primarily on external vendor claims.
Existing regulatory frameworks adequately address risks associated with real-time clinical AI decision support.
Professional clinician-only social networks and forums contribute useful insights that improve clinical decision-making.
The benefits of patient engagement with health information on social media outweigh concerns about misinformation for clinical care decisions.
Integration of AI or algorithmic outputs into clinician documentation and order entry systems increases overall administrative accuracy.
Data privacy protections for connected consumer health apps are sufficient for their use in supporting clinical care or risk assessment.
Broad adoption of AI in administrative processes (such as scheduling and coding) reduces operational costs without introducing measurable new errors.
Clinician judgments are more reliable than AI predictions when both are available for the same clinical question.
Standardized national evaluation metrics for clinical AI tools should be established before they are widely used in patient care.
Patient exposure to health content on social media platforms has a net positive effect on health literacy.
Verification of healthcare provider identity on discussion platforms increases the clinical usefulness of shared information.
