Bibliographic record
Abstract
While artificial intelligence (AI) has the potential to improve healthcare delivery and outcomes for all individuals, including women, there are several areas where gender disparities persist, and AI may be involved in mitigating or exacerbating them. Amid the ever-increasing pervasiveness of AI in the health sector, it is imperative to examine how AI intersects with gender disparities in health outcomes globally, especially in the early adopter countries. Gender disparities in health outcomes are a crucial concern that gets manifested in differences in health status, access to healthcare, and health-related experiences between men and women. Gender disparities in health outcomes highlight unequal distributions of health resources, opportunities, and risks based on gender. This study looked at how investments in healthcare that uses AI affect the health outcomes of women in early adopter countries (the United States, the United Kingdom, Canada, France, Germany, China, Japan, and Israel) between 2014 and 2023. Multivariate analysis of variance (MANOVA) was used to assess the cumulative effect of AI investments on health outcomes of women. The study found that the F-value (4.139) is low and not statistically significant (p = 0.376), highlighting the fact that even in early adopter countries, investments in biotechnology, pharmaceuticals, and healthcare AI do not have a statistically substantial bearing on the overall health outcomes for women, making gender disparity in the countries. These findings show that in order to guarantee quantifiable and significant gains in health outcomes, AI investments in healthcare must take a more focused and evidence-based approach.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".