Artificial Intelligence-Based Analysis of Laparoscopic Imaging for Intraoperative Surgical Decision Support
Clinical Snapshot
PICO Framework
| P — Population | Surgeons performing laparoscopic procedures across surgical specialties |
| I — Intervention | AI-based analysis of laparoscopic video streams for surgical decision support |
| C — Comparator | Standard laparoscopic surgery guided by surgeon visual interpretation alone |
| O — Outcomes | Surgical quality, patient safety, intraoperative decision-making accuracy |
Bottom Line
This narrative review examines AI applications for laparoscopic surgical decision support, concluding that feasibility has been demonstrated in research settings. However, the review lacks systematic methodology, quantitative synthesis, and quality assessment typical of high-quality evidence reviews. While the technology shows promise for enhancing surgical decision-making through objective video analysis, the evidence presented is insufficient to support immediate clinical implementation. The field remains in early translational stages, with clinical applications potentially emerging over the next decade. Australian clinicians should await more robust systematic reviews and randomised controlled trials before considering adoption, alongside appropriate regulatory approval and cost-effectiveness evaluation.
Key Findings
Effect Size: Not quantified
Primary Outcome: Feasibility of AI-based surgical scene and process understanding
Nnt Or Sensitivity: Not reported - feasibility study outcomes only
Confidence Interval: Not reported
Clinical Application
Currently limited to research settings; clinical translation anticipated within next decade Would require TGA approval for clinical use; potential integration with existing laparoscopic equipment in Australian hospitals; cost-effectiveness analysis needed for PBS consideration Surgeons performing laparoscopic procedures across multiple specialties
Abstract
Across surgical specialties, minimally invasive (laparoscopic) surgery has become a standard technique, as it is associated with less trauma, reduced postoperative complication rates, and quicker recovery for patients as compared with open surgery. Due to the limited field of view and limited haptic feedback, the surgical decision-making process in laparoscopic surgery is currently guided solely by surgeons' visual interpretation of the laparoscopic video stream. Modern artificial intelligence (AI) methods excel at the interpretation of visual data and find applications in clinical routine in fields such as radiology and endoscopy. AI methods could help augment laparoscopic surgery through objective real-time analysis of the laparoscopic video stream. Research studies have demonstrated the feasibility of AI-based surgical scene and process understanding. This review provides an overview of these AI applications, focusing on approaches that could, in the next decade, be translated into intraoperative surgical decision support tools for increased surgical quality and patient safety.
References
- 1.Zhang, Z., Mascagni, P., Reinke, A., De Backer, P., Bogaert, W., Mezzina, M., Donoho, D. A., & Kolbinger, F. R. (2026). Artificial Intelligence-Based Analysis of Laparoscopic Imaging for Intraoperative Surgical Decision Support. Annual Review of Biomedical Engineering, 28. https://doi.org/10.1146/annurev-bioeng-110824-114236
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