TRV-2026-2003Certified recordPeer-reviewed

Assessment of Perceptions, Trust, and Adoption of Clinical Artificial Intelligence Tools Among Physicians and Dentists, Riyadh, Saudi Arabia

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…

Health · Problem Space — documented harm · certified 2026-10-20 · v1 · article view · machine-readable

Current reading — problem

Although most surveyed clinicians know clinical AI tools, trust in them is generally low, especially among physicians, which limits perceptions and adoption intention.

What this doesn’t fix

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.

Evidence

Reader signal

How should this claim be treated?

Cite this record

Truvace Impact Record TRV-2026-2003, v1: “Assessment of Perceptions, Trust, and Adoption of Clinical Artificial Intelligence Tools Among Physicians and Dentists, Riyadh, Saudi Arabia.” Truvace, 2026-10-20. /record/TRV-2026-2003 (accessed at citation time). sha256 520f3aadfdcca343…

Calibration history

Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.

  1. Certifiedv1520f3aadfdcc…

    Certified into the record

Verify this record
How to verify without trusting this page

Fetch the canonical text of any version from /api/record/TRV-2026-2003 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.