Awareness and Perspectives on the Role of Artificial Intelligence in Primary Care: Survey of Rural and Urban Primary Care Physicians in Alberta, Canada
Bibliographic record
Abstract
Background: Artificial intelligence (AI) is increasingly used in healthcare to enhance patient care and diagnostic efficiency. Despite its potential, physicians’ awareness and perspectives on AI are not well-studied. AI in medicine is often seen as image-focused, but it also includes online appointment scheduling, digitizing medical records, digital consultations, and drug dosage algorithms. We surveyed Canadian primary care physicians (PCPs) to assess their awareness and perspectives on AI in healthcare. Methods: A population-based, cross-sectional survey study was administered to Alberta family medicine physicians in both urban and rural healthcare centers. The survey was administered online using Qualtrics. The survey was distributed by email and through newsletters. Results: Out of 79 responses, 46 were complete and analyzed. Most physicians worked in urban settings and had no prior AI training. Rural physicians showed higher interest and comfort in using AI. Knowledge of AI varied among physicians, with limited awareness of subset concepts of AI. Amongst respondents, the most used AI tools were an ECG interpreter and a language translator, and physicians expressed interest in various AI tools for medical use. Conclusion: There is a lack of knowledge and use of AI tools in medicine, with both urban and rural physicians’ responses suggesting a need for more education and training in AI. Lack of human connection was the main fear that was expressed regarding the use of AI in healthcare. This survey's findings may inform future research into the development and implementation of AI in primary care.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 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".