Surgical computer vision for intraoperative decision-support: a scoping review on performance metrics and readiness for real-time deployment

Abstract

Background: Real-time computer vision-based artificial intelligence (CV-AI) systems for surgical video analysis are rapidly advancing. Current evaluation strategies and clinical-readiness reporting, however, remain inconsistent. This scoping review mapped contemporary CV-AI task domains, performance metrics, and evidence of readiness for real-time intraoperative deployment within general surgery.

Methods: This study followed Joanna Briggs Institute methodology for scoping reviews, and was reported in accordance with PRISMA-ScR. Eligible studies were identified by systematic literature search of the MEDLINE, Embase, PubMed, and Scopus databases. All studies published between 1 June 2015 and 1 June 2025 were eligible.

Results: A total of 490 articles were screened, with 113 studies meeting the inclusion criteria after full-text review. Retrospective feasibility analyses predominated, with only 13 studies (12%) evaluating real-time intraoperative integration. Five task domains were identified (phase recognition, anatomy identification, action-event recognition, instrument tracking, and skill-assessment). Forty-one unique performance metrics were reported, with predominant use of discrimination-style summary measures (e.g., accuracy, recall, F1 score), and comparatively sparse reporting of class imbalance, boundary-aware (e.g., Hausdorff distance) or real-time workflow factors (e.g., latency/stability, interface design, surgeon feedback). External validation was described in 13 (12%) studies. Nine studies (8%) referenced artificial intelligence-specific reporting frameworks.

Conclusion: Surgical CV-AI is advancing technically, but remains predominantly at an early feasibility stage. Variability in current metric application and limited real-time clinical evaluation limit potential for comparability, applicability and widespread adoption. Standardised metrics, evaluation frameworks, prospective clinical trials, and collaborative end-user engagement are critical to translate conceptual promise to reliable real-time decision-support tools that support surgeon judgement and integrate seamlessly into routine operative workflows.

Keywords

Artificial intelligence / computer vision / deep learning / surgical video analysis / intraoperative decision support / minimally invasive surgery / laparoscopic surgery

References

[1] Ward TM,Ban Y.Computer vision in surgery.Surgery2021;169:1253-6

[2] Kourounis G,Thomson B,Ugail H.Computer image analysis with artificial intelligence: a practical introduction to convolutional neural networks for medical professionals.Postgrad Med J2023;99:1287-94 PMCID:PMC10658730

[3] Fernicola A,Capuano M,Aprea G.Artificial intelligence applied to laparoscopic cholecystectomy: what is the next step?.Updates Surg2024;76:1655-67 PMCID:PMC11455722

[4] Kankanamge D,Ong Z.Artificial intelligence based assessment of minimally invasive surgical skills using standardised objective metrics - a narrative review.Am J Surg2025;241:116074

[5] Garrow CR,Li L.Machine learning for surgical phase recognition: a systematic review.Ann Surg2021;273:684-93

[6] Kehagias D,Bellou A.Detection of anatomic landmarks during laparoscopic cholecystectomy with the use of artificial intelligence - a systematic review of the literature.Updates Surg2026;78:229-40

[7] Mascagni P,Sestini L.Computer vision in surgery: from potential to clinical value.NPJ Digit Med2022;5:163 PMCID:PMC9616906

[8] Varghese C,O’Grady G.Artificial intelligence in surgery.Nat Med2024;30:1257-68

[9] Zhou R,Zhang H.Vision techniques for anatomical structures in laparoscopic surgery: a comprehensive review.Front Surg2025;12:1557153 PMCID:PMC12034692

[10] Reinke A,Baumgartner M.Understanding metric-related pitfalls in image analysis validation.Nat Methods2024;21:182-94 PMCID:PMC11181963

[11] Miller C,Nyaga DM.A review of model evaluation metrics for machine learning in genetics and genomics.Front Bioinform2024;4:1457619 PMCID:PMC11420621

[12] Taha AA.Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool.BMC Med Imaging2015;15:29 PMCID:PMC4533825

[13] Müller D,Kramer F.Towards a guideline for evaluation metrics in medical image segmentation.BMC Res Notes2022;15:210 PMCID:PMC9208116

[14] Khojah B,Malibari A,Sabbagh A.Deep learning-based intraoperative guidance system for anatomical identification in laparoscopic surgery: a review.J Infrastruct Policy Dev2024;8:8939

[15] Ibrahim AM.What metrics accurately reflect surgical quality?.Annu Rev Med2018;69:481-91

[16] Maier-Hein L,Godau P.Metrics reloaded: recommendations for image analysis validation.Nat Methods2024;21:195-212 PMCID:PMC11182665

[17] Kitaguchi D,Madani A.Computer Vision in Surgery International CollaborativeArtificial intelligence for computer vision in surgery: a call for developing reporting guidelines.Ann Surg2022;275:e609-11

[18] Aromataris E,Porritt K,Jordan Z. JBI Manual for Evidence Synthesis. JBI; 2024. Available from https://synthesismanual.jbi.global [accessed 25 Feburary 2026].

[19] Tricco AC,Zarin W.PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation.Ann Intern Med2018;169:467-73

[20] Pollock D,Khalil H.Recommendations for the extraction, analysis, and presentation of results in scoping reviews.JBI Evid Synth2023;21:520-32

[21] Abbing JR,Gerats BGA,Milletari F.Towards an AI-based assessment model of surgical difficulty during early phase laparoscopic cholecystectomy.Comput Methods Biomech Biomed Eng Imaging Vis2023;11:1299-306

[22] Al Abbas AI,Radi I.The development of a deep learning model for automated segmentation of the robotic pancreaticojejunostomy.Surg Endosc2024;38:2553-61

[23] Alkhamaiseh KN,Shebrain S.Towards reliable hepatocytic anatomy segmentation in laparoscopic cholecystectomy using U-Net with Auto-Encoder.Surg Endosc2023;37:7358-69

[24] Aoyama Y,Etoh T.Artificial intelligence for surgical safety during laparoscopic gastrectomy for gastric cancer: indication of anatomical landmarks related to postoperative pancreatic fistula using deep learning.Surg Endosc2024;38:5601-12

[25] Arpaia P,Corcione F.Assessment of blood perfusion quality in laparoscopic colorectal surgery by means of Machine Learning.Sci Rep2022;12:14682 PMCID:PMC9424219

[26] Aspart F,Lavanchy JL.ClipAssistNet: bringing real-time safety feedback to operating rooms.Int J Comput Assist Radiol Surg2022;17:5-13 PMCID:PMC8739308

[27] Bamba Y,Itabashi M,Okamoto T.Automated recognition of objects and types of forceps in surgical images using deep learning.Sci Rep2021;11:22571 PMCID:PMC8604928

[28] Bamba Y,Itabashi M.Object and anatomical feature recognition in surgical video images based on a convolutional neural network.Int J Comput Assist Radiol Surg2021;16:2045-54 PMCID:PMC8224261

[29] Bamba Y,Itabashi M.Automated bleeding identification in surgical videos using deep learning.Tokyo Women Med Univ J2022;6:117-25

[30] Ban Y,Eckhoff JA.SUPR-GAN: SUrgical PRediction GAN for event anticipation in laparoscopic and robotic surgery.IEEE Robot Autom Lett2022;7:5741-8

[31] Beyersdorffer P,Jansen K.Detection of adverse events leading to inadvertent injury during laparoscopic cholecystectomy using convolutional neural networks.Biomed Tech2021;66:413-21

[32] Chen G,Yang B.Artificial intelligence model for perigastric blood vessel recognition during laparoscopic radical gastrectomy with D2 lymphadenectomy in locally advanced gastric cancer.BJS Open2024;9 PMCID:PMC11833313

[33] Chen H,Fang Z.Artificial intelligence assisted real-time recognition of intra-abdominal metastasis during laparoscopic gastric cancer surgery.NPJ Digit Med2025;8:9 PMCID:PMC11701130

[34] Cheng K,Wu S.Artificial intelligence-based automated laparoscopic cholecystectomy surgical phase recognition and analysis.Surg Endosc2022;36:3160-8

[35] den Boer RB,de Jongh C.Deep learning-based recognition of key anatomical structures during robot-assisted minimally invasive esophagectomy.Surg Endosc2023;37:5164-75 PMCID:PMC10322962

[36] Derathé A,Moreau-Gaudry A,Gibaud B.Predicting the quality of surgical exposure using spatial and procedural features from laparoscopic videos.Int J Comput Assist Radiol Surg2020;15:59-67

[37] Endo Y,Mori Y.Impact of AI system on recognition for anatomical landmarks related to reducing bile duct injury during laparoscopic cholecystectomy.Surg Endosc2023;37:5752-9 PMCID:PMC10322759

[38] Fer D,Abukhalil R.An artificial intelligence model that automatically labels roux-en-Y gastric bypasses, a comparison to trained surgeon annotators.Surg Endosc2023;37:5665-72

[39] Fujinaga A,Etoh T.Development of a cross-artificial intelligence system for identifying intraoperative anatomical landmarks and surgical phases during laparoscopic cholecystectomy.Surg Endosc2023;37:6118-28

[40] Furube T,Kawakubo H.Usefulness of an artificial intelligence model in recognizing recurrent laryngeal nerves during robot-assisted minimally invasive esophagectomy.Ann Surg Oncol2024;31:9344-51

[41] Ghobadi V,Wan Hasan WZ.Real-time robust liver and gallbladder segmentation during laparoscopic cholecystectomy using convolutional neural networks: an analysis.Art Int Surg2024;4:278-87

[42] Golany T,Freedman D.Artificial intelligence for phase recognition in complex laparoscopic cholecystectomy.Surg Endosc2022;36:9215-23 PMCID:PMC9652206

[43] Guédon ACP,Osman KNMMH.Deep learning for surgical phase recognition using endoscopic videos.Surg Endosc2021;35:6150-7

[44] Han F,Zhi S.Artificial intelligence recognition system of pelvic autonomic nerve during total mesorectal excision.Dis Colon Rectum2025;68:308-15 PMCID:PMC11837963

[45] Hashimoto DA,Witkowski ER.Computer vision analysis of intraoperative video: automated recognition of operative steps in laparoscopic sleeve gastrectomy.Ann Surg2019;270:414-21 PMCID:PMC7216040

[46] Hegde SR,Iyengar N.Automated segmentation of phases, steps, and tasks in laparoscopic cholecystectomy using deep learning.Surg Endosc2024;38:158-70

[47] Honda R,Ishikawa Y.Deep learning-based surgical step recognition for laparoscopic right-sided colectomy.Langenbecks Arch Surg2024;409:309

[48] Horita K,Itatani Y.Real-time detection of active bleeding in laparoscopic colectomy using artificial intelligence.Surg Endosc2024;38:3461-9

[49] Igaki T,Kojima S.Artificial intelligence-based total mesorectal excision plane navigation in laparoscopic colorectal surgery.Dis Colon Rectum2022;65:e329-33

[50] Igaki T,Matsuzaki H.Automatic surgical skill assessment system based on concordance of standardized surgical field development using artificial intelligence.JAMA Surg2023;158:e231131 PMCID:PMC10248810

[51] Jin Y,Chen C,Dou Q.Temporal memory relation network for workflow recognition from surgical video.IEEE Trans Med Imaging2021;40:1911-23

[52] Kawamura M,Fujinaga A.Development of an artificial intelligence system for real-time intraoperative assessment of the Critical View of Safety in laparoscopic cholecystectomy.Surg Endosc2023;37:8755-63

[53] Khalid MU,Masino C.Use of artificial intelligence for decision-support to avoid high-risk behaviors during laparoscopic cholecystectomy.Surg Endosc2023;37:9467-75

[54] Khojah B,Saleem A.Deep learning-based intraoperative visual guidance model for ureter identification in laparoscopic sigmoidectomy.Surg Endosc2025;39:3610-23

[55] Kinoshita K,Kobayashi N.An artificial intelligence-based nerve recognition model is useful as surgical support technology and as an educational tool in laparoscopic and robot-assisted rectal cancer surgery.Surg Endosc2024;38:5394-404 PMCID:PMC11362368

[56] Kirtac K,Lavanchy JL.Surgical phase recognition: from public datasets to real-world data.Appl Sci2022;12:8746

[57] Kitaguchi D,Matsuzaki H.Automated laparoscopic colorectal surgery workflow recognition using artificial intelligence: experimental research.Int J Surg2020;79:88-94

[58] Kitaguchi D,Matsuzaki H.Real-time automatic surgical phase recognition in laparoscopic sigmoidectomy using the convolutional neural network-based deep learning approach.Surg Endosc2020;34:4924-31

[59] Kitaguchi D,Matsuzaki H,Hasegawa H.Development and validation of a 3-dimensional convolutional neural network for automatic surgical skill assessment based on spatiotemporal video analysis.JAMA Netw Open2021;4:e2120786 PMCID:PMC8363914

[60] Kitaguchi D,Hayashi K.Development and validation of a model for laparoscopic colorectal surgical instrument recognition using convolutional neural network-based instance segmentation and videos of laparoscopic procedures.JAMA Netw Open2022;5:e2226265 PMCID:PMC9391983

[61] Kitaguchi D,Matsuzaki H.Real-time vascular anatomical image navigation for laparoscopic surgery: experimental study.Surg Endosc2022;36:6105-12

[62] Kitaguchi D,Kosugi N.Artificial intelligence for the recognition of key anatomical structures in laparoscopic colorectal surgery.Br J Surg2023;110:1355-8

[63] Kojima S,Igaki T.Deep-learning-based semantic segmentation of autonomic nerves from laparoscopic images of colorectal surgery: an experimental pilot study.Int J Surg2023;109:813-20 PMCID:PMC10389575

[64] Kolbinger FR,Jenke AC.Anatomy segmentation in laparoscopic surgery: comparison of machine learning and human expertise - an experimental study.Int J Surg2023;109:2962-74 PMCID:PMC10583931

[65] Kolbinger FR,Carstens M.Artificial Intelligence for context-aware surgical guidance in complex robot-assisted oncological procedures: an exploratory feasibility study.Eur J Surg Oncol2024;50:106996

[66] Komatsu M,Yura M.Automatic surgical phase recognition-based skill assessment in laparoscopic distal gastrectomy using multicenter videos.Gastric Cancer2024;27:187-96

[67] Korndorffer JR Jr,Spain DA.Situating artificial intelligence in surgery: a focus on disease severity.Ann Surg2020;272:523-8

[68] Kumazu Y,Kitamura N.Automated segmentation by deep learning of loose connective tissue fibers to define safe dissection planes in robot-assisted gastrectomy.Sci Rep2021;11:21198 PMCID:PMC8551298

[69] Kumazu Y,Senya S.AI-based visualization of loose connective tissue as a dissectable layer in gastrointestinal surgery.Sci Rep2025;15:152 PMCID:PMC11695969

[70] Laplante S,Kiani P.Validation of an artificial intelligence platform for the guidance of safe laparoscopic cholecystectomy.Surg Endosc2023;37:2260-8

[71] Lavanchy JL,Kirtac K.Automation of surgical skill assessment using a three-stage machine learning algorithm.Sci Rep2021;11:5197 PMCID:PMC7933408

[72] Lavanchy JL,Dall’Alba D.Challenges in multi-centric generalization: phase and step recognition in Roux-en-Y gastric bypass surgery.Int J Comput Assist Radiol Surg2024;19:2249-57 PMCID:PMC11541311

[73] Leifman G,Rivlin E,Assalia A.Real-time artificial intelligence validation of critical view of safety in laparoscopic cholecystectomy.Intell-Based Med2024;10:100153

[74] Liu K,Shi P,Song H.Real-time surgical tool detection in computer-aided surgery based on enhanced feature-fusion convolutional neural network.J Comput Des Eng2022;9:1123-34

[75] Liu R,Wang Z,Liu J.Artificial intelligence in laparoscopic cholecystectomy: does computer vision outperform human vision?.Art Int Surg2022;2:80-92

[76] Liu Z,Zheng L.SINet: a hybrid deep CNN model for real-time detection and segmentation of surgical instruments.Biomed Signal Process Control2024;88:105670

[77] Loukas C,Schizas D.Patch-based classification of gallbladder wall vascularity from laparoscopic images using deep learning.Int J Comput Assist Radiol Surg2021;16:103-13

[78] Madani A,Altieri MS.Artificial intelligence for intraoperative guidance: using semantic segmentation to identify surgical anatomy during laparoscopic cholecystectomy.Ann Surg2022;276:363-9 PMCID:PMC8186165

[79] Mascagni P,Urade T.A computer vision platform to automatically locate critical events in surgical videos: documenting safety in laparoscopic cholecystectomy.Ann Surg2021;274:e93-5

[80] Mascagni P,Laracca GG.Multicentric validation of EndoDigest: a computer vision platform for video documentation of the critical view of safety in laparoscopic cholecystectomy.Surg Endosc2022;36:8379-86

[81] Mascagni P,Alapatt D.Artificial intelligence for surgical safety: automatic assessment of the critical view of safety in laparoscopic cholecystectomy using deep learning.Ann Surg2022;275:955-61

[82] Mascagni P,Lapergola A.Early-stage clinical evaluation of real-time artificial intelligence assistance for laparoscopic cholecystectomy.Br J Surg2024;111:znad353

[83] Mita K,Takahashi K.Anatomical recognition of dissection layers, nerves, vas deferens, and microvessels using artificial intelligence during transabdominal preperitoneal inguinal hernia repair.Hernia2024;29:52 PMCID:PMC11671561

[84] Nakajima K,Takenaka S.Automated surgical skill assessment in colorectal surgery using a deep learning-based surgical phase recognition model.Surg Endosc2024;38:6347-55

[85] Nakajima K,Kitaguchi D.Artificial intelligence assessment of tissue-dissection efficiency in laparoscopic colorectal surgery.Langenbecks Arch Surg2025;410:80 PMCID:PMC11845557

[86] Nakanuma H,Fujinaga A.An intraoperative artificial intelligence system identifying anatomical landmarks for laparoscopic cholecystectomy: a prospective clinical feasibility trial (J-SUMMIT-C-01).Surg Endosc2023;37:1933-42

[87] Nakamura T,Kumazu Y.Precise highlighting of the pancreas by semantic segmentation during robot-assisted gastrectomy: visual assistance with artificial intelligence for surgeons.Gastric Cancer2024;27:869-75

[88] Narihiro S,Hasegawa H,Ito M.Deep learning-based real-time ureter identification in laparoscopic colorectal surgery.Dis Colon Rectum2024;67:e1596-9

[89] Oh N,Kim T,Kim J.Real-time segmentation of biliary structure in pure laparoscopic donor hepatectomy.Sci Rep2024;14:22508 PMCID:PMC11439027

[90] Orimoto H,Ikeda S.Development of an artificial intelligence system to indicate intraoperative findings of scarring in laparoscopic cholecystectomy for cholecystitis.Surg Endosc2025;39:1379-87 PMCID:PMC11794413

[91] Ortenzi M,Antolin A.A novel high accuracy model for automatic surgical workflow recognition using artificial intelligence in laparoscopic totally extraperitoneal inguinal hernia repair (TEP).Surg Endosc2023;37:8818-28 PMCID:PMC10615930

[92] Owen D,Luengo I.Automated identification of critical structures in laparoscopic cholecystectomy.Int J Comput Assist Radiol Surg2022;17:2173-81

[93] Petracchi EJ,Varela J.Use of artificial intelligence in the detection of the critical view of safety during laparoscopic cholecystectomy.J Gastrointest Surg2024;28:877-9

[94] Pradeep CS.Surgical phase classification and operative skill assessment through spatial context aware CNNs and time-invariant feature extracting autoencoders.Biocybern Biomed Eng2023;43:700-24

[95] Ramesh S,Gonzalez C.Multi-task temporal convolutional networks for joint recognition of surgical phases and steps in gastric bypass procedures.Int J Comput Assist Radiol Surg2021;16:1111-9 PMCID:PMC8260406

[96] Ryu S,Kitagawa T.Real-time artificial intelligence navigation-assisted anatomical recognition in laparoscopic colorectal surgery.J Gastrointest Surg2023;27:3080-2 PMCID:PMC10837241

[97] Ryu K,Nakajima K.Deep learning-based vessel automatic recognition for laparoscopic right hemicolectomy.Surg Endosc2024;38:171-8

[98] Ryu S,Goto K.Feasibility of simultaneous artificial intelligence-assisted and nir fluorescence navigation for anatomical recognition in laparoscopic colorectal surgery.J Fluoresc2025;35:6755-61

[99] Ryu S,Goto K.Artificial intelligence-enhanced navigation for nerve recognition and surgical education in laparoscopic colorectal surgery.Surg Endosc2025;39:1388-96 PMCID:PMC11794642

[100] Sasaki K,Kobayashi S.Automated surgical workflow identification by artificial intelligence in laparoscopic hepatectomy: experimental research.Int J Surg2022;105:106856

[101] Sasaki S,Takenaka S.Machine learning-based automatic evaluation of tissue handling skills in laparoscopic colorectal surgery: a retrospective experimental study.Ann Surg2023;278:e250-5

[102] Sato K,Matsuzaki H.Real-time detection of the recurrent laryngeal nerve in thoracoscopic esophagectomy using artificial intelligence.Surg Endosc2022;36:5531-9

[103] Sengun B,Tataroglu Ozbulak GA.Artificial intelligence in minimally invasive adrenalectomy: using deep learning to identify the left adrenal vein.Surg Laparosc Endosc Percutan Tech2023;33:327-31

[104] Sengun B,Yazici ZA.Utilization of artificial intelligence in minimally invasive right adrenalectomy: recognition of anatomical landmarks with deep learning.Acta Chir Belg2024;124:492-8

[105] Sharma S,Pestana Legori L.Early operative difficulty assessment in laparoscopic cholecystectomy via snapshot-centric video analysis.Int J Comput Assist Radiol Surg2025;20:1185-93

[106] Shi P,Liu K.Attention-based spatial-temporal neural network for accurate phase recognition in minimally invasive surgery: feasibility and efficiency verification.J Comput Des Eng2022;9:406-16

[107] Shi J,Wang Z.Deep learning HRNet FCN for blood vessel identification in laparoscopic pancreatic surgery.NPJ Digit Med2025;8:235 PMCID:PMC12046043

[108] Shinozuka K,Fujinaga A.Artificial intelligence software available for medical devices: surgical phase recognition in laparoscopic cholecystectomy.Surg Endosc2022;36:7444-52 PMCID:PMC9485170

[109] Smithmaitrie P,Sae-Lim W,Jearanai S.Development of deep learning framework for anatomical landmark detection and guided dissection line during laparoscopic cholecystectomy.Heliyon2024;10:e25210 PMCID:PMC10847946

[110] Strong JS,Takeuchi M.Evaluating surgical expertise with AI-based automated instrument recognition for robotic distal gastrectomy.Ann Gastroenterol Surg2024;8:611-9 PMCID:PMC11216797

[111] Strong JS,Takeuchi M.External validation of an automated surgical step recognition model for robotic distal gastrectomy (RDG) using a multicenter dataset.Ann Gastroenterol Surg2025;9:1155-62 PMCID:PMC12586924

[112] Sunakawa T,Kobayashi S.Deep learning-based automatic bleeding recognition during liver resection in laparoscopic hepatectomy.Surg Endosc2024;38:7656-62

[113] Takeuchi M,Ndagijimana A.Automatic surgical phase recognition in laparoscopic inguinal hernia repair with artificial intelligence.Hernia2022;26:1669-78

[114] Takeuchi M,Saito K.Automated surgical-phase recognition for robot-assisted minimally invasive esophagectomy using artificial intelligence.Ann Surg Oncol2022;29:6847-55

[115] Takeuchi M,Lipps C.Towards automatic verification of the critical view of the myopectineal orifice with artificial intelligence.Surg Endosc2023;37:4525-34

[116] Takeuchi M,Tsuji T.Evaluation of surgical complexity by automated surgical process recognition in robotic distal gastrectomy using artificial intelligence.Surg Endosc2023;37:4517-24 PMCID:PMC9949687

[117] Tao H,Zeng X.Artificial intelligence-based anatomy recognition in laparoscopic hepatectomy: multicentre study.Br J Surg2024;111:znae254

[118] Tashiro Y,Kobayashi N.Novel navigation for laparoscopic cholecystectomy fusing artificial intelligence and indocyanine green fluorescent imaging.J Hepatobiliary Pancreat Sci2024;31:305-7

[119] Tokuyasu T,Matsunobu Y.Development of an artificial intelligence system using deep learning to indicate anatomical landmarks during laparoscopic cholecystectomy.Surg Endosc2021;35:1651-8 PMCID:PMC7940266

[120] Tomioka K,Kobayashi N.Development of a novel artificial intelligence system for laparoscopic hepatectomy.Anticancer Res2023;43:5235-43

[121] Twinanda AP,Mutter D,de Mathelin M.EndoNet: a deep architecture for recognition tasks on laparoscopic videos.IEEE Trans Med Imaging2017;36:86-97

[122] Wagner M,Kisilenko A.Comparative validation of machine learning algorithms for surgical workflow and skill analysis with the HeiChole benchmark.Med Image Anal2023;86:102770

[123] Ward TM,Ban Y,Meireles OR.Artificial intelligence prediction of cholecystectomy operative course from automated identification of gallbladder inflammation.Surg Endosc2022;36:6832-40

[124] Wu S,Liu R.Youth Committee of Pancreatic Disease of Sichuan Doctor Association (YCPD)SurgSmart: an artificial intelligent system for quality control in laparoscopic cholecystectomy: an observational study.Int J Surg2023;109:1105-14 PMCID:PMC10389595

[125] Yamazaki Y,Matsuda T.Automated surgical instrument detection from laparoscopic gastrectomy video images using an open source convolutional neural network platform.J Am Coll Surg2020;230:725-732e1

[126] Yang HY,Yoon J.Deep learning-based surgical phase recognition in laparoscopic cholecystectomy.Ann Hepatobiliary Pancreat Surg2024;28:466-73 PMCID:PMC11599821

[127] Yen HH,Yang MH.Automated surgical action recognition and competency assessment in laparoscopic cholecystectomy: a proof-of-concept study.Surg Endosc2025;39:3006-16

[128] Yin SM,Chiu IM.Deep learning implementation for extrahepatic bile duct detection during indocyanine green fluorescence-guided laparoscopic cholecystectomy: pilot study.BJS Open2025;9 PMCID:PMC11928939

[129] Yoshida M,Takeshita N.Surgical step recognition in laparoscopic distal gastrectomy using artificial intelligence: a proof-of-concept study.Langenbecks Arch Surg2024;409:213

[130] You J,Wang Y.Artificial intelligence automated surgical phases recognition in intraoperative videos of laparoscopic pancreatoduodenectomy.Surg Endosc2024;38:4894-905

[131] Zang C,Narasimhan S.Surgical phase recognition in inguinal hernia repair-ai-based confirmatory baseline and exploration of competitive models.Bioengineering2023;10 PMCID:PMC10295003

[132] Zhai Y,Zheng Z.Artificial intelligence for automatic surgical phase recognition of laparoscopic gastrectomy in gastric cancer.Int J Comput Assist Radiol Surg2024;19:345-53

[133] Zygomalas A,Katsiakis N,Skroubis G.Artificial intelligence assisted recognition of anatomical landmarks and laparoscopic instruments in transabdominal preperitoneal inguinal hernia repair.Surg Innov2024;31:178-84

[134] Meireles OR,Altieri MS.SAGES Video Annotation for AI Working GroupsSAGES consensus recommendations on an annotation framework for surgical video.Surg Endosc2021;35:4918-29

[135] Maier-Hein L,Kozubek M.BIAS: transparent reporting of biomedical image analysis challenges.Med Image Anal2020;66:101796 PMCID:PMC7441980

[136] Bossuyt PM,Bruns DE.STARD GroupSTARD 2015: an updated list of essential items for reporting diagnostic accuracy studies.BMJ2015;351:h5527 PMCID:PMC4623764

[137] Sounderajah V,Golub RM.STARD-AI Steering CommitteeDeveloping a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol.BMJ Open2021;11:e047709 PMCID:PMC8240576

[138] Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP; STROBE Initiative. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies.BMJ2007;335:806-8 PMCID:PMC2034723

[139] Ogrinc G,Goodman D,Davidoff F.SQUIRE 2.0 (Standards for QUality Improvement Reporting Excellence): revised publication guidelines from a detailed consensus process.BMJ Qual Saf2016;25:986-92 PMCID:PMC5256233

[140] Mongan J,Kahn CE Jr.Checklist for Artificial Intelligence in Medical Imaging (CLAIM): a guide for authors and reviewers.Radiol Artif Intell2020;2:e200029 PMCID:PMC8017414

[141] Tejani AS,Gatti AA.CLAIM 2024 Update PanelChecklist for Artificial Intelligence in Medical Imaging (CLAIM): 2024 Update.Radiol Artif Intell2024;6:e240300 PMCID:PMC11304031

[142] Norgeot B,Beaulieu-Jones BK.Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist.Nat Med2020;26:1320-4 PMCID:PMC7538196

[143] Vasey B,Campbell B.DECIDE-AI expert groupReporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI.BMJ2022;377:e070904 PMCID:PMC9116198

[144] Rivera S, Liu X, Chan AW, Denniston AK, Calvert MJ; SPIRIT-AI and CONSORT-AI Working Group. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension.Lancet Digit Health2020;2:e549-60 PMCID:PMC8212701

[145] Liu X,Moher D,Denniston AK.SPIRIT-AI and CONSORT-AI Working GroupReporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension.BMJ2020;370:m3164 PMCID:PMC8183333

[146] Collins GS,Dhiman P.TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods.BMJ2024;385:e078378 PMCID:PMC11019967

[147] Lekadir K,Porras AR.FUTURE-AI ConsortiumFUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare.BMJ2025;388:e081554 PMCID:PMC11795397

[148] Park SH,Jang HY.Methods for clinical evaluation of artificial intelligence algorithms for medical diagnosis.Radiology2023;306:20-31

[149] Rex DK,O’Brien M.The American Society for Gastrointestinal Endoscopy PIVI (Preservation and Incorporation of Valuable Endoscopic Innovations) on real-time endoscopic assessment of the histology of diminutive colorectal polyps.Gastrointest Endosc2011;73:419-22

[150] Mascagni P,Sestini L.Applications of artificial intelligence in surgery: clinical, technical, and governance considerations.Cir Esp2024;102 Suppl 1:S66-71

[151] Kitaguchi D,Hasegawa H.Artificial intelligence-based computer vision in surgery: recent advances and future perspectives.Ann Gastroenterol Surg2022;6:29-36 PMCID:PMC8786689

[152] Mascagni P.OR black box and surgical control tower: recording and streaming data and analytics to improve surgical care.J Visc Surg2021;158:S18-25

[153] McGivern KG,Knight SR.Applying artificial intelligence to big data in hepatopancreatic and biliary surgery: a scoping review.Art Int Surg2023;3:27-47

[154] Adegbesan A,Aremu O,Amamdikwa JC.From scalpels to algorithms: the risk of dependence on artificial intelligence in surgery.J Med Surg Public Health2024;3:100140

[155] Hwang N,Kirkpatrick J,Koea JB.Educational quality of Robotic Whipple videos on YouTube.HPB2024;26:826-32

[156] Byrd IV TF.Artificial intelligence in surgery - a narrative review.J Med Artif Intell2024;7:29

[157] Ergina PL,McCulloch P,Altman DG.IDEAL GroupIDEAL framework for surgical innovation 2: observational studies in the exploration and assessment stages.BMJ2013;346:f3011 PMCID:PMC3685514

[158] McCulloch P,Altman DG,Diener MK.IDEAL GroupIDEAL framework for surgical innovation 1: the idea and development stages.BMJ2013;346:f3012 PMCID:PMC3685515

[159] Marcus HJ,Khan DZ.IDEAL Robotics ColloquiumThe IDEAL framework for surgical robotics: development, comparative evaluation and long-term monitoring.Nat Med2024;30:61-75

[160] Roodbeen SX,Hirst A.Evolution of transanal total mesorectal excision according to the IDEAL framework.BMJ Surg Interv Health Technol2019;1:e000004 PMCID:PMC8749305

[161] Cook JA,Blazeby JM,Marinac-Dabic D.IDEAL GroupIDEAL framework for surgical innovation 3: randomised controlled trials in the assessment stage and evaluations in the long term study stage.BMJ2013;346:f2820 PMCID:PMC3685513

[162] Li X,Jiang Y.Vision-Language Models in medical image analysis: from simple fusion to general large models.Inform Fusion2025;118:102995

[163] Li X,Lin J,Feng T.The synergy of seeing and saying: revolutionary advances in multi-modality medical vision-language large models.Artif Intell Sci Eng2025;1:79-97