Artificial Intelligence (AI) and the future of Iran’s Primary Health Care (PHC) system
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: The rapid adoption of Artificial Intelligence (AI) in health service delivery underscores the need for awareness, preparedness, and strategic utilization of AI's potential to optimize Primary Health Care (PHC) systems. This study aims to equip Iran's PHC system for AI integration by envisioning potential futures while addressing past challenges and recognizing current trends. METHOD: This study developed a conceptual framework based on the "Future Triangle" (FT) and the "Health Systems Governance" (HSG) models. This framework delineates the characteristics associated with the 'pulls on the future' for desired and intelligent PHC, as identified by a panel of experts. Additionally, the 'weights of the past'-referring to the challenges faced by Iran's PHC system in utilizing AI-, and the 'push of the present'-which captures the impacts of AI implementation in global primary care settings-were extracted through a review of relevant literature. The integration and analysis of the collected evidence facilitated the formulation of a range of potential future scenarios, including both optimistic and pessimistic scenarios. FINDINGS: The interaction between the three elements of the FT will shape the future states of Iran's PHC, whether optimistic or pessimistic. Building an optimistic scenario for an AI-driven PHC system necessitates addressing past challenges, including deficiencies in the referral and family doctor systems, the absence of evidence-based decision-making, neglect of essential community health needs, fragmented service delivery, high provider workload, and inadequate follow-up on the health status of service recipients. Consideration must also be given to the current impacts of AI in primary care, including comprehensive, coordinated, and need-based service delivery with systematic and integrated monitoring, quality improvement, early disease prevention, precise diagnosis, and effective treatment. Furthermore, fostering a shared vision among stakeholders by defining and advocating for a future system characterized by foresight, resilience, agility, adaptability, and collaboration is essential. CONCLUSION: Envisioning potential future states requires a balanced consideration of the influence of past, present, and future, recognizing the dual potential of AI to drive either positive or negative outcomes. Achieving the optimistic future or the "utopia of intelligent PHC" and avoiding the pessimistic future or the "dystopia of intelligent PHC" requires coherent planning, attention to the tripartite considerations of the future, past, and present, and a clear understanding of the roles, expectations, and needs of stakeholders.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it