Artificial Intelligence in Urology—A Survey of Urology Healthcare Providers
Bibliographic record
Abstract
Background/Objectives: Artificial intelligence (AI) has been utilised in urological conditions such as urolithiasis, urogynaecology and uro-oncology. The aim of this study is to examine the attitudes and beliefs about AI technology amongst urology healthcare providers. Methods: A structured online questionnaire, created from a modified Delphi method with a panel of urologists and urology surgical trainees, was delivered through the Urological Asia Association’s annual congress. The questionnaire, with 25 items of mixed type responses (five-point Likert scale, nominal-polytomous and open-ended), acquired data regarding demographics, perception and attitudes towards general usage of AI in urological care. Results: A total of 464 respondents from 47 different countries were collected. The results showed that 83.4% of participants believed AI will improve efficiency and 18.8% believed they are knowledgeable in AI technology, with ordinal logistic regression showing both urology specialists and trainees are more likely to agree to these responses. Overall, 51.5% believed AI adoption will not replace clinical practice, and regression analysis found those with previous AI training are more likely to agree to this response. We found AI is commonly used in research, patient education and administrative tasks and identified key enablers as regulatory approval, AI clinical effectiveness and access to AI training. Conclusions: Overall attitudes and beliefs towards the use of AI in urology is positive and encouraging. AI training and education and regulatory reform needs to be addressed to allow integration of AI into clinical practice. A limitation of the study lies in its generalisability to global settings due to the demographics of the respondents.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".