Artificial intelligence for surgical scene understanding: a systematic review and reporting quality meta-analysis
Carstens, Matthias Vasisht, Shubha Zhang, Zheyuan Barbur, Iulia Reinke, Annika Maier-Hein, Lena Hashimoto, Daniel A. Kolbinger, Fiona R.
Carstens, Matthias
Vasisht, Shubha
Zhang, Zheyuan
Barbur, Iulia
Reinke, Annika
Maier-Hein, Lena
Hashimoto, Daniel A.
Kolbinger, Fiona R.
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Department
Computer Vision
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Journal article
Date
2025
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English
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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.
Citation
M. Carstens et al., “Artificial intelligence for surgical scene understanding: a systematic review and reporting quality meta-analysis,” npj Digital Medicine 2025 9:1, vol. 9, no. 1, pp. 59-, Dec. 2025, doi: 10.1038/s41746-025-02227-4
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npj Digital Medicine
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Springer Nature