Multinational attitudes towards AI in healthcare and diagnostics among hospital patients
Bibliographic record
Abstract
Abstract The successful implementation of artificial intelligence (AI) in healthcare is dependent upon the acceptance of this technology by key stakeholders, particularly patients, who are the primary beneficiaries of AI-driven outcomes. This international, multicenter, cross-sectional study assessed the attitudes of hospital patients towards AI in healthcare across 43 countries. A total of 13806 patients at 74 hospitals were surveyed between February and November 2023, with 64.8% from the Global North and 35.2% from the Global South. The findings indicate a predominantly favorable general view of AI in healthcare, with 57.6% of respondents expressing a positive attitude. However, attitudes exhibited notable variation based on demographic characteristics, health status, and technological literacy. Female respondents and those with poorer health status exhibited fewer positive attitudes towards AI use in medicine. Conversely, higher levels of AI knowledge and frequent use of technology devices were associated with more positive attitudes. It is noteworthy that less than half of the participants expressed positive attitudes regarding all items pertaining to trust in AI. The lowest level of trust was observed for the accuracy of AI in providing information regarding treatment responses. Patients exhibited a strong preference for explainable AI and physician-led decision-making, even if it meant slightly compromised accuracy. This large-scale, multinational study provides a comprehensive perspective on patient attitudes towards AI in healthcare across six continents. Findings suggest a need for tailored AI implementation strategies that consider patient demographics, health status, and preferences for explainable AI and physician oversight. All study data has been made publicly available to encourage replication and further investigation.
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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 | 0.003 | 0.008 |
| 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) | 0.003 | 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".