Hazrah P, Rautela VS, Sharma V, Mittal S, Madan RR, Sharma D. Improving precision of laparoscopic cholecystectomy: A review on the role of newer intraoperative imaging aids and artificial intelligence. Artif Intell Gastrointest Endosc 2026; 7(2): 121644 [DOI: 10.37126/aige.121644]
Corresponding Author of This Article
Priya Hazrah, Professor, Department of Surgery, Lady Hardinge Medical College, Shaheed Bhagat Singh Marg, New Delhi 110001, Delhi, India. priya.hazrah39@lhmc-hosp.gov.in
Research Domain of This Article
Surgery
Article-Type of This Article
review-article
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This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Artificial Intelligence in Gastrointestinal Endoscopy
ISSN
2689-7164
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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
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Hazrah P, Rautela VS, Sharma V, Mittal S, Madan RR, Sharma D. Improving precision of laparoscopic cholecystectomy: A review on the role of newer intraoperative imaging aids and artificial intelligence. Artif Intell Gastrointest Endosc 2026; 7(2): 121644 [DOI: 10.37126/aige.121644]
Priya Hazrah, Varun Singh Rautela, Sonali Mittal, Ravi Raj Madan, Department of Surgery, Lady Hardinge Medical College, New Delhi 110001, Delhi, India
Vishal Sharma, Business Consulting and Account Management, Sognos Solutions, Melbourne 3000, Victoria, Australia
Deborshi Sharma, Department of Surgery, Atal Bihari Vajpayee Institute of Medical Sciences, New Delhi 110001, Delhi, India
Author contributions: Hazrah P contributed to conceptualization of the manuscript; Hazrah P, Rautela VS, Mittal S, and Madan RR contributed to operative photograph data acquisition; Hazrah P, Rautela VS, Sharma V, Mittal S, Madan RR, and Sharma D contributed to the literature review, writing manuscript, preparation of tables and referencing; Hazrah P, Sharma V contributed to AI assisted anatomic segmentation, preparation of figures, legends and final editing; all authors have read and approved the final manuscript.
AI contribution statement: The author(s) would like to acknowledge the use of Gemini (Google AI) for assistance in performing preliminary literature review searches and for generating schematic illustrations of surgical anatomy based on the author’s original intraoperative photographs. No AI tool was involved in the generation of research data, interpretation of results, or formulation of conclusions. All AI-generated outputs were critically reviewed and revised by the authors.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: Priya Hazrah, Professor, Department of Surgery, Lady Hardinge Medical College, Shaheed Bhagat Singh Marg, New Delhi 110001, Delhi, India. priya.hazrah39@lhmc-hosp.gov.in
Received: April 1, 2026 Revised: May 19, 2026 Accepted: June 15, 2026 Published online: September 8, 2026 Processing time: 157 Days and 14.5 Hours
Abstract
Bile duct injuries have been the main area of concern in laparoscopic cholecystectomy (LC). Adoption of strategies such as identification of important anatomical landmarks, critical view of safety (CVS) achievement, careful use of energy sources, timeout and bailout techniques in difficult cases have evolved to increase the safety of LC. However, bile duct injury rates are yet to equal to that of open cholecystectomy. Newer technological advances in endovision systems, like indocyanine green near infra-red fluorescence cholangiography, yellow enhancement and integration of artificial intelligence (AI) appear to be promising. Evolving evidence has suggested the incorporation of AI-driven systems may improve safety of LC such as “go” and “no-go” zone indication, CVS assessment, surgical phase recognition, predicting surgical difficulty, automated coaching and training. This narrative review deliberates upon the current evidence in literature related to use of newer imaging aids and AI in LC.
Core Tip: Endovision technology coupled with multispectral imaging, such as indocyanine green near infra-red, yellow enhancement, and artificial intelligence (AI), has potential to improve the precision of laparoscopic cholecystectomy by enhancing tissue differentiation. AI architectures can predict difficult cholecystectomy, assess surgical workflow, achieve anatomic segmentation, identify safe dissection zones, and assist in coaching/training. By leveraging multispectral imaging along with advanced AI architecture-convolutional neural networks for segmentation, graph neural networks for relational anatomical mapping, and transformers for temporal workflow analytics-surgical platforms can achieve context-aware surgery, ultimately bridging the gap between intraoperative imaging and enhanced patient safety through automated surgical coaching and decision support.
