The AI doctor will see you now: public perspectives on artificial intelligence in healthcare
Bibliographic record
Abstract
Objectives: The use of artificial intelligence (AI) in healthcare is a growing field of research and clinical application. The views of the general public, that is, current and future healthcare users, need to be surveyed and interpreted so that researchers and the public have a shared understanding of the appropriate use of AI. Currently, there are only limited data on the public's views. The aim of this study is to understand the public's perspective on the use of AI in healthcare. Methods: An anonymous, quantitative questionnaire was administered as part of a public exhibition on AI. The questionnaire contained 8 questions based on previously validated subject areas designed to assess respondents' views on the use of AI in healthcare. Brief demographic data were also collected. Results: The population surveyed was more diverse and younger than the general UK population (64% White, 45% aged 18-29). Respondents were largely comfortable with the application of AI in healthcare: 80% felt positively about its use, 56% thought it would be safe. Seventy-one percent did not feel that it would replace doctors, and most would not be happy for AI to make decisions without considering their feelings. Conclusions: Our study shows that the subset of the general public we surveyed, largely comprised of young, likely future healthcare users, is comfortable with the use of AI in healthcare, but does not see it as a replacement for doctors. Advances in knowledge: This article highlights views from a subset of the general public on the use of AI in healthcare, which is largely under researched.
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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 | 0.023 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".