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TRUVACE RECORD VERSION
record: TRV-2026-2003
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-20T10:42:20.348226Z
status: published
lens: p_space
sector: health
headline: Assessment of Perceptions, Trust, and Adoption of Clinical Artificial Intelligence Tools Among Physicians and Dentists, Riyadh, Saudi Arabia
dek: Clinical artificial intelligence (AI) tools are increasingly integrated into healthcare; however, their acceptance varies across professional groups. Understanding clinicians' familiarity with and perceptions, trust, and adoption of clinical AI tools is essential for safe and effective implementation. This study assessed physicians' and dentists' perceptions, trust, and intention to adopt clinical AI tools in governmental university hospitals in Riyadh, Saudi Arabia. A cross-sectional survey was conducted among…
gain_title: (none)
problem_title: Although most surveyed clinicians know clinical AI tools, trust in them is generally low, especially among physicians, which limits perceptions and adoption intention.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Although most surveyed clinicians know clinical AI tools, trust in them is generally low, especially among physicians, which limits perceptions and adoption intention.
problem_evidence: Trust was generally low (mean 3.1-3.6), particularly among physicians | Despite high awareness, perceptions of clinical AI tools and intention to adopt them remain moderate and constrained by low trust.
quick_read: A cross-sectional online survey of 400 physicians and dentists at three large governmental university hospitals in Riyadh measured familiarity with, perceptions of, trust in, and intention to adopt clinical AI tools. Familiarity was high at 89.5%, while perceptions and adoption intention were moderate and trust was generally low.

The pattern suggests that awareness alone does not produce confident use of clinical AI. Scores differed by profession, training level, specialty, and hospital digital maturity. The authors point to training, transparent systems, and regulatory support, but the survey records attitudes and intentions, not actual use or patient outcomes.
limitation: The evidence comes from a cross-sectional survey of clinicians at three governmental university hospitals in Riyadh, so it captures self-reported perceptions and intentions at one point in time in a specific setting.
tag: Evidence-backed problem
key_points: The survey covered 400 clinicians at King Saud University Medical City, King Abdulaziz Medical City-National Guard, and King Abdullah Bin Abdulaziz University Hospital. | Physicians reported greater familiarity and more positive perceptions than dentists, and higher adoption intention. | Residents, physicians, and clinicians in digitally advanced hospitals had significantly higher perception, trust, and adoption scores (p <0.05). | Work setting, resident status, and physician specialty were the strongest predictors of higher scores in the regression analysis.
rundown: The study used a structured online questionnaire with high reliability across subscales (Cronbach's α: 0.880-0.952). Analysis combined descriptive statistics, non-parametric tests, and multivariate linear regression.

Mean perception scores ranged from 3.2 to 3.8 on a 5-point scale, trust scores from 3.1 to 3.6, and adoption intention from 3.5 to 3.9. Physicians' trust was particularly low, whereas dentists reported moderate trust.

The authors conclude that targeted training, transparent systems, and regulatory support are essential for integrating clinical AI into practice at these tertiary academic centers.
sources:
- peer_reviewed | Journal of Multidisciplinary Healthcare | https://doi.org/10.2147/jmdh.s635220 | 2026-10-13
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