The Impact of Artificial Intelligence on Healthcare: Perspectives and Approaches for Latin America and the Caribbean
Bibliographic record
Abstract
This technical document, produced by the Center for Implementation and Innovation in Health Policies (CIIPS) of the Institute forof Clinical and Health Effectiveness (IECS), focuses on the impact of Artificial Intelligence (AI) on healthcare in Latin America and the Caribbean (LAC). It provides an exploratory analysis of the current state of AI implementation in the region, identifying challenges and opportunities. The document highlights the heterogeneity and fragmentation of AI projects in LAC, mainly concentrated at the meso and micro-management levels. Absence of AI governance, regulations, and electronic health records are among the major barriers faced. However, there is growing interest and investment in AI by healthcare providers and technology companies, suggesting potential for future development. The importance of AI education for healthcare professionals, research to drive innovation, and the collaborative role of tools like ChatGPT in supporting the healthcare system are discussed. Overall, the document concludes that AI in healthcare in LAC is in an early stage of maturity, with ample opportunities for improvement through collaboration and the adoption of policies and regulations to facilitate integration and sustainable development in the region.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".