Theoretical Foundations of Integrating Artificial Intelligence into Preventive Care Models in Family Medicine
Bibliographic record
Abstract
This study critically explores the theoretical foundations guiding the integration of artificial intelligence (AI) into preventive care models within family medicine. While AI offers substantial benefits in early disease prediction, risk stratification, and decision support, the research reveals a persistent gap between technical implementation and theoretical, ethical, and contextual frameworks. Most studies rely heavily on electronic health records (EHRs) from high-income countries, neglecting cultural diversity and real-world applicability. The Technology Acceptance Model (TAM), Human-Centered AI (HCAI), and Explainable AI (XAI) frameworks offer valuable guidance but are inconsistently applied. Moreover, participatory design and equity-focused strategies are largely absent, raising concerns about inclusivity and long-term sustainability. The findings underscore the need for multi-theoretical, ethically grounded, and human-centered approaches to ensure that AI systems not only function effectively but also align with the holistic and patient-focused values of family medicine.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.034 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".