Modelos de inteligência artificial na atenção primária: desempenho, transparência e segurança na triagem de pacientes
Bibliographic record
Abstract
The incorporation of artificial intelligence (AI) into health systems has significantly progressed in recent years, expanding into non-hospital settings such as primary care and emergency services. This article critically analyzes international experiences (United States, Canada, United Kingdom, and Brazil) involving AI applications for automated triage, risk stratification, and clinical decision support, with a focus on low- and medium-complexity healthcare settings. It outlines key risks associated with these technologies—algorithmic bias, opacity, interoperability failures, data governance weaknesses, and privacy issues—in light of international regulatory and ethical frameworks proposed by institutions such as the World Health Organization (WHO), the Food and Drug Administration (FDA), and the National Institute for Health and Care Excellence (NICE). Based on this analysis, the article proposes a set of minimum criteria for the safe and ethical implementation of AI in primary care and emergency contexts, including local clinical validation, transparency, bias control, data governance, systemic integration, staff training, and post-deployment monitoring. It concludes that AI can strengthen primary care, improve patient flow management, and support complex clinical decisions, provided it is implemented under robust clinical governance, with continuous professional oversight and full respect for patients’ rights.
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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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".