A Review of the Intraoperative Use of Artificial Intelligence in Urologic Surgery
Bibliographic record
Abstract
Introduction: Future evolutions of artificial intelligence (AI) will support autonomous surgery, conducted without the need for human decision making and implementation, but we have not yet achieved this level of technology. Presently, the predominant applications of AI in urological surgery are achieved using the tool of computer vision. This review aims to summarise potential intra-operative AI tools for urologists. Method: A systematic search was conducted through Scopus, PubMed, Embase, and Medline by two independent reviewers, with a third to resolve any conflicts. As a rule, only original articles describing the use or potential use of artificial intelligence intra-operatively in urologic surgery were included. A total of 60 articles were reviewed. Key content and findings: There is significant research investigating the ability to diagnose bladder tumours using AI assistance at the time of cystoscopy, with studies showing the ability to also grade tumour based on appearance and differentiate between carcinoma in situ and indeterminate lesions. With the aid of AI, kidney stones can accurately be identified and diagnosed morphologically intra-operatively. Various studies show the ability to overlay 2D and 3D anatomical models on a surgeon’s screen, as well as correctly identify important anatomical landmarks and surgical instruments, with AI support. All types of intra-operative data can be analysed with AI to assess surgeon performance, predict post-operative outcomes such as continence post prostatectomy, and recognise complications such as bleeding and ischemia. Conclusions: AI holds great potential for urologists during surgery to improve safety, diagnostic accuracy, identification of anatomical structures and surgical instruments, assessment of the surgeon for self-evaluation, and prediction of post-operative outcomes. Before the use of AI as an aid during surgery becomes standard practice, more prospective studies are needed to evaluate its real-world application, feasibility, and costs.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".