Bibliographic record
Abstract
Objectives The rapid adoption of AI tools like ChatGPT has transformed information access, particularly in healthcare. However, engagement with AI may be influenced by factors such as healthcare accessibility and educational resources, with potential implications for misinformation in low-resource settings. This study investigates the relationship between physician density, tertiary education enrollment, and national interest in ChatGPT. Methods A cross-sectional analysis was conducted using global datasets. Physician density, tertiary education enrollment, GDP, and internet penetration were sourced from WHO, UNESCO, and the World Bank, respectively. The primary outcome, ChatGPT interest scores, was derived from Google Trends. Pearson correlation and multiple linear regression analyses were used to explore associations, controlling for GDP and internet penetration. Logistic regression was employed as a sensitivity analysis, categorizing variables into high and low groups. Results Data from 100 countries were analyzed. A significant negative correlation was observed between physician density and ChatGPT interest (r = -0.32, p = 0.012). Multiple linear regression confirmed that lower physician density was significantly associated with higher ChatGPT interest (β = -0.2857, p = 0.045). Tertiary education enrollment showed no significant association with ChatGPT interest. Logistic regression supported these findings, with higher physician density significantly reducing the likelihood of high ChatGPT interest (OR = 0.214, p = 0.001). Conclusion Our study suggests that regions with fewer healthcare professionals may engage more with AI tools like ChatGPT, highlighting the need for careful integration of AI into healthcare systems to prevent misinformation and support equitable access to reliable health information. Public Interest Summary It is well known that people who have difficulty in accessing healthcare may turn to the internet for medical advice, but it is not yet known if artificial intelligence, like ChatGPT, is being adopted by users for this same purpose. Given the widespread use of ChatGPT, this study explored whether ChatGPT interest in different countries was related to the number of physicians in those countries. We found that in countries with fewer doctors per capita, public interest in ChatGPT tends to be higher. While this does not confirm that people are using ChatGPT specifically for medical advice, it raises important questions about how AI may be filling gaps in access to healthcare. Given the potential for AI to spread inaccurate information, these findings highlight the need for careful regulation to ensure AI tools are used responsibly and do not contribute to misinformation in healthcare.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".