ArticleNPJ digital medicine2025
Artificial intelligence for surgical scene understanding: a systematic review and reporting quality meta-analysis.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Implementation determinants and outcomes of artificial intelligence in surgical healthcare: a systematic review protocol.BMJ open · 2026Article
- Artificial Intelligence for Automated Recognition of Hepatocystic Anatomy During Laparoscopic Cholecystectomy: Current Evidence, Clinical Readiness, and Future Directions.Medicina (Kaunas, Lithuania) · 2026Review
- Reply to: Surgical scene understanding and the emerging challenge of independent validation in an industry-led AI ecosystem.NPJ digital medicine · 2026Article
- Surgical scene understanding and the structural validation gap in an industry-led AI ecosystem.NPJ digital medicine · 2026Article
- External validation and clinical readiness of intraoperative video AI in general surgery: a systematic review.Surgery today · 2026Review
- Privacy-Preserving Surgical Video Analysis with Swarm Learning - Results from a Multinational Appendectomy Cohort.NEJM AI · 2026Article
- Appendix300: Surgical video and patient metadata of 330 laparoscopic appendectomy cases from five institutions.Scientific data · 2026Article
- Surgeons' perceptions of artificial intelligence (AI) for gaze guidance in laparoscopic cholecystectomy.Surgical endoscopy · 2026Article
- Optimizing AI implementation for surgery: recommendations for infrastructure and deployment in the operating room.NPJ digital medicine · 2026Article
- Decentralized, privacy-preserving surgical video analysis with Swarm Learning.medRxiv : the preprint server for health sciences · 2025Article
- Appendix300: A multi-institutional laparoscopic appendectomy video dataset for computational modeling tasks.medRxiv : the preprint server for health sciences · 2025Article
Corrections and comments
- Update of
Authors and funding
8 authors.
Funding
Abstract
Surgical scene understanding (SSU) uses artificial intelligence (AI) to interpret visual data from surgeries, such as laparoscopic videos. Despite promising foundational research on instrument and anatomy recognition, clinical adoption remains minimal. This systematic review and meta-analysis (PROSPERO: CRD420251005301) evaluates current SSU research in minimally invasive abdominal surgery, focusing on data curation, model design, validation, reporting standards, and clinical relevance. A total of 188 studies were reviewed. Most relied on small, single-center datasets (70.7%), primarily laparoscopic cholecystectomies (59.0%), reflecting an overall narrow topical breadth. Validation practices were often weak, rarely involving external datasets (10.1%) or clinical experts. Few studies addressed clinical translation (5.9%), model performance variability estimation (38.3%), or made code available (29.8%). Overall, limited progress toward clinical integration has been made over the past decade. Our findings highlight the need for diverse, multi-institutional datasets, robust validation practices, and clinically driven development to unlock the full potential of SSU in surgical practice.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.