Understanding Physician Attitudes Toward AI in Clinical Decision-Making: Cross-Sectional Study
Bibliographic record
Abstract
Background: The Kingdom of Saudi Arabia (KSA) has made tremendous efforts to promote the adoption of advanced technologies such as artificial intelligence (AI). While the successful adoption of AI is dependent on physician perception, there is a scarcity of data concerning KSA physicians' perception of the technology. Objective: The purpose of this study was to conduct a cross-sectional survey that would provide updated statistics on physicians' attitudes toward AI with a focus on ethical and practical perspectives among physicians licensed in the KSA. Methods: A pilot study was conducted with 10 physicians to enhance the clarity of the survey questions. The pilot was followed by a cross-sectional survey, which was conducted through 25 online, self-administered questionnaires hosted on Qualtrics. A total of 218 physicians filled out the survey. The dataset was then exported into Microsoft Excel and analyzed using descriptive statistics rather than inferential analyses given the exploratory nature of this study and its primary aim to generate updated descriptive insights rather than test specific hypotheses. Results: A total of 201 fully filled surveys, representing 127 (63.2%) female and 74 (36.8%) male physicians with experience ranging from 3 to ≥30 years, were analyzed. Most physicians (n=165, 82.1%) trusted AI-based clinical decision-making, and 76.6% (n=154) believed that the technology improved efficiency in health care delivery. Unfortunately, only 25.9% (n=52) of physicians had used AI in the previous year. Common barriers to AI adoption included lack of training, high implementation costs, and resistance to change, as well as concerns related to privacy, data security, bias in AI-based recommendations, patient autonomy, and liability. Participants recommended training through workshops (n=50, 25%), online courses (n=47, 23.4%), hands-on experience (n=44, 21.9%), and a combination of online courses and hands-on experience (n=17, 8.5%). Conclusions: Physicians who responded to this survey supported AI's use in health care but reported facing financial, ethical, and training barriers, which could be addressed through informed consent and staff training.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".