Artificial intelligence in primary care: innovation at a crossroads
Bibliographic record
Abstract
Despite being a cornerstone of health-care delivery, primary care is increasingly under strain. The latest advancements in artificial intelligence (AI) offer new opportunities to transform primary care. However, the rapid deployment of AI ahead of robust real-world evaluation or regulation raises concerns about unintended consequences on the quality of care. We review applications of AI in primary care, covering AI to support primary care providers and people with their health. This Review considers the impact of AI applications on different domains of health-care quality-effectiveness, safety, timeliness, efficiency, patient-centred care, health-care provider experience, equity, and planetary health-and on the primary care-specific attributes of accessibility, comprehensiveness, coordination, and continuity. Implementation of AI in primary care benefits from careful consideration of these quality domains, a focus on universal design principles, digital determinants of health, and AI health literacy, and alignment with patient experiences and values, to support the transformation towards sustainable and high-quality AI-enabled primary care.
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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 | 0.024 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".