Factors Shaping Healthcare Professionals’ Perceptions of AI in Saudi Arabia: A Cross-Sectional Study
Bibliographic record
Abstract
The successful adoption of artificial intelligence (AI) in healthcare relies on healthcare professionals' perceptions of its usefulness and their preparedness to integrate it into their practice. This study explores factors influencing these perceptions, focusing on demographic characteristics, computer skills, and AI knowledge. A cross-sectional study was conducted among healthcare professionals in Saudi Arabia between October 2023 and May 2024. Data were collected using a questionnaire that assessed perceptions of AI's professional impact (FACTOR 1) and preparedness to use AI (FACTOR 2) by using the Shinners Artificial Intelligence Perception (SHAIP) scale. Mann-Whitney tests examined differences in FACTOR 1 and FACTOR 2 by computer skills and AI knowledge. Multivariable linear regression identified predictors of these perceptions. Of the 359 participants, 76.60% reported high computer skills, while 62.12% reported low AI knowledge. Participants with higher computer skills and greater AI knowledge scored significantly higher on both FACTOR 1 and FACTOR 2 (p < 0.05). Gender, involvement in health informatics, and experience with healthcare technology emerged as significant predictors. Female participants reported significantly lower perceptions of AI's professional impact compared to males (β = -0.253, p = 0.020). Participants working in health informatics demonstrated a significantly better perception of AI's professional impact, while professionals with more than five years of experience using healthcare technology scored higher on both factors. In conclusion, digital competencies and AI knowledge are critical for shaping healthcare professionals' perceptions of AI. Targeted interventions and policy to enhance these skills are essential to promote equitable and effective AI adoption in healthcare settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".