Awareness and Perspectives on the Role of Artificial Intelligence in Primary Care: A cross-Sectional Survey of Rural and Urban Primary Care Physicians in Alberta, Canada
Bibliographic record
Abstract
Background: Artificial intelligence (AI) is increasingly integrated into healthcare, yet physicians’ awareness and perspectives remain underexplored. While often associated with imaging, AI applications also include online scheduling, digitized records, virtual consultations, and drug dosage algorithms. This study surveyed Canadian primary care physicians (PCPs) to assess their awareness and attitudes toward AI in healthcare. Methods: A cross-sectional survey was distributed via email and newsletters to family physicians across Alberta, including both urban and rural settings. Responses were collected through Qualtrics. Results: Of 79 responses, 46 met inclusion criteria. Most respondents practiced in urban areas (63%) and had no prior AI training (65%). Rural physicians reported greater comfort and interest in AI, including its use for monitoring treatment adherence (p=0.043) and analyzing EMR data for health management (p=0.027). Knowledge of AI varied widely: only 30% recognized that deep learning involves artificial neural networks, while 44% reported no knowledge of the concept. Commonly used AI tools included ECG interpreters (65%) and language translators (37%). Physicians showed interest in expanded medical uses of AI. 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. The “Lack of human connection” was the main fear that was expressed regarding the use of AI in healthcare suggesting concerns about potential impacts on patient-provider relationships. 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".