REFERENCES

1. Meara JG, Leather AJ, Hagander L, et al. Global Surgery 2030: evidence and solutions for achieving health, welfare, and economic development. Lancet. 2015;386:569-624.

2. Nepogodiev D, Martin J, Biccard B, Makupe A, Bhangu A; National Institute for Health Research Global Health Research Unit on Global Surgery. Global burden of postoperative death. Lancet. 2019;393:401.

3. Keil H, Beisemann N, Swartman B, et al. Intraoperative revision rates due to three-dimensional imaging in orthopedic trauma surgery: results of a case series of 4721 patients. Eur J Trauma Emerg Surg. 2023;49:373-81.

4. Wang X, Yang J, Zhou B, Tang L, Liang Y. Integrating mixed reality, augmented reality, and artificial intelligence in complex liver surgeries: enhancing precision, safety, and outcomes. ILIVER. 2025;4:100167.

5. Singla N, Dubey K, Srivastava V. Automated assessment of breast cancer margin in optical coherence tomography images via pretrained convolutional neural network. J Biophotonics. 2019;12:e201800255.

6. Hollon TC, Pandian B, Adapa AR, et al. Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks. Nat Med. 2020;26:52-8.

7. Li Y, Zhao Z, Li R, Li F. Deep learning for surgical workflow analysis: a survey of progresses, limitations, and trends. Artif Intell Rev. 2024;57:10929.

8. Kitaguchi D, Takeshita N, Matsuzaki H, et al. Real-time automatic surgical phase recognition in laparoscopic sigmoidectomy using the convolutional neural network-based deep learning approach. Surg Endosc. 2020;34:4924-31.

9. Aboy M, Minssen T, Vayena E. Navigating the EU AI Act: implications for regulated digital medical products. NPJ Digit Med. 2024;7:237.

10. Magalhães R, Oliveira A, Terroso D, et al. Mixed reality in the operating room: a systematic review. J Med Syst. 2024;48:76.

11. Nair M, Svedberg P, Larsson I, Nygren JM. A comprehensive overview of barriers and strategies for AI implementation in healthcare: mixed-method design. PLoS One. 2024;19:e0305949.

12. Ennab M, Mcheick H. Enhancing interpretability and accuracy of AI models in healthcare: a comprehensive review on challenges and future directions. Front Robot AI. 2024;11:1444763.

13. Salahuddin Z, Woodruff HC, Chatterjee A, Lambin P. Transparency of deep neural networks for medical image analysis: a review of interpretability methods. Comput Biol Med. 2022;140:105111.

14. U. S. Food & Drug Administration. FDA Roundup: September 17, 2024. Available from: https://www.fda.gov/news-events/press-announcements/fda-roundup-september-17-2024?utm_source=chatgpt.com [accessed 16 December 2025].

15. Schneider C, Thompson S, Totz J, et al. Comparison of manual and semi-automatic registration in augmented reality image-guided liver surgery: a clinical feasibility study. Surg Endosc. 2020;34:4702-11.

16. Koetzier LR, Wu J, Mastrodicasa D, et al. Generating synthetic data for medical imaging. Radiology. 2024;312:e232471.

17. PRISMA 2020. PRISMA statement. Available from: https://www.prisma-statement.org/ [accessed 16 December 2025].

18. Sasaki K, Ito M, Kobayashi S, et al. Automated surgical workflow identification by artificial intelligence in laparoscopic hepatectomy: experimental research. Int J Surg. 2022;105:106856.

19. Padovan E, Marullo G, Tanzi L, et al. A deep learning framework for real-time 3D model registration in robot-assisted laparoscopic surgery. Int J Med Robot. 2022;18:e2387.

20. Boonkong A, Khampitak K, Kaewfoongrungsi P, Namkhun S, Hormdee D. Applying deep learning for occluded uterus and fallopian tube detection for laparoscopic tubal sterilization. IEEE Access. 2024;12:183182-94.

21. Liu Z, Li W, Li H, et al. Automated deep neural network-based identification, localization, and tracking of cardiac structures for ultrasound-guided interventional surgery. J Thorac Dis. 2023;15:2129-40.

22. Aubreville M, Knipfer C, Oetter N, et al. Automatic classification of cancerous tissue in laserendomicroscopy images of the oral cavity using deep learning. Sci Rep. 2017;7:11979.

23. Nwoye CI, Yu T, Sharma S, et al. CholecTriplet2022: Show me a tool and tell me the triplet - an endoscopic vision challenge for surgical action triplet detection. Med Image Anal. 2023;89:102888.

24. Zhang X, Sisniega A, Zbijewski WB, et al. Combining physics-based models with deep learning image synthesis and uncertainty in intraoperative cone-beam CT of the brain. Med Phys. 2023;50:2607-24.

25. Caballas KG, Bolingot HJM, Libatique NJC, Tangonan GL. Development of a visual guidance system for laparoscopic surgical palpation using computer vision. 2020 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES); 2021 Mar 1-3; Langkawi Island, Malaysia. New York: IEEE; 2021. pp. 88-93.

26. Smithmaitrie P, Khaonualsri M, Sae-Lim W, Wangkulangkul P, Jearanai S, Cheewatanakornkul S. Development of deep learning framework for anatomical landmark detection and guided dissection line during laparoscopic cholecystectomy. Heliyon. 2024;10:e25210.

27. Török P, Harangi B. Digital image analysis with fully connected convolutional neural network to facilitate hysteroscopic fibroid resection. Gynecol Obstet Invest. 2018;83:615-9.

28. Lin J, Clancy NT, Qi J, et al. Dual-modality endoscopic probe for tissue surface shape reconstruction and hyperspectral imaging enabled by deep neural networks. Med Image Anal. 2018;48:162-76.

29. Blokker M, Hamer PCW, Wesseling P, Groot ML, Veta M. Fast intraoperative histology-based diagnosis of gliomas with third harmonic generation microscopy and deep learning. Sci Rep. 2022;12:11334.

30. Mojahed D, Ha RS, Chang P, et al. Fully automated postlumpectomy breast margin assessment utilizing convolutional neural network based optical coherence tomography image classification method. Acad Radiol. 2020;27:e81-6.

31. Tai Y, Qian K, Huang X, Zhang J, Jan MA, Yu Z. Intelligent intraoperative haptic-AR navigation for COVID-19 lung biopsy using deep hybrid model. IEEE Trans Industr Inform. 2021;17:6519-27.

32. Jalal NA, Alshirbaji TA, Docherty PD, et al. Laparoscopic video analysis using temporal, attention, and multi-feature fusion based-approaches. Sensors. 2023;23:1958.

33. Mao Z, Das A, Islam M, et al. PitSurgRT: real-time localization of critical anatomical structures in endoscopic pituitary surgery. Int J Comput Assist Radiol Surg. 2024;19:1053-60.

34. Zeng Y, Xu S, Chapman WC Jr, et al. Real-time colorectal cancer diagnosis using PR-OCT with deep learning. Theranostics. 2020;10:2587-96.

35. Tanzi L, Piazzolla P, Porpiglia F, Vezzetti E. Real-time deep learning semantic segmentation during intra-operative surgery for 3D augmented reality assistance. Int J Comput Assist Radiol Surg. 2021;16:1435-45.

36. Podlasek J, Heesch M, Podlasek R, Kilisiński W, Filip R. Real-time deep learning-based colorectal polyp localization on clinical video footage achievable with a wide array of hardware configurations. Endosc Int Open. 2021;9:E741-8.

37. Sato K, Fujita T, Matsuzaki H, et al. Real-time detection of the recurrent laryngeal nerve in thoracoscopic esophagectomy using artificial intelligence. Surg Endosc. 2022;36:5531-9.

38. Canalini L, Klein J, Miller D, Kikinis R. Segmentation-based registration of ultrasound volumes for glioma resection in image-guided neurosurgery. Int J Comput Assist Radiol Surg. 2019;14:1697-713.

39. Mekki L, Sheth NM, Vijayan RC, et al. Surgical navigation for guidewire placement from intraoperative fluoroscopy in orthopedic surgery. Phys Med Biol. 2023;68:215001.

40. Geldof F, Pruijssers CWA, Jong LS, Veluponnar D, Ruers TJM, Dashtbozorg B. Tumor segmentation in colorectal ultrasound images using an ensemble transfer learning model: towards intra-operative margin assessment. Diagnostics. 2023;13:3595.

41. Kenig N, Monton Echeverria J, Muntaner Vives A. Artificial intelligence in surgery: a systematic review of use and validation. J Clin Med. 2024;13:7108.

42. Yang HY, Hong SS, Yoon J, et al. Deep learning-based surgical phase recognition in laparoscopic cholecystectomy. Ann Hepatobiliary Pancreat Surg. 2024;28:466-73.

43. Kostiuchik G, Sharan L, Mayer B, Wolf I, Preim B, Engelhardt S. Surgical phase and instrument recognition: how to identify appropriate dataset splits. Int J Comput Assist Radiol Surg. 2024;19:699-711.

44. Goyal A, Mendoza M, Munoz AE, et al. Artificial intelligence for real-time surgical phase recognition in minimal invasive inguinal hernia repair: a systematic review on behalf of TROGSS - the robotic global surgical society. Art Int Surg. 2025;5:450-64.

45. Bellini V, Russo M, Domenichetti T, Panizzi M, Allai S, Bignami EG. Artificial intelligence in operating room management. J Med Syst. 2024;48:19.

46. Hollon TC, Orringer DA. An automated tissue-to-diagnosis pipeline using intraoperative stimulated Raman histology and deep learning. Mol Cell Oncol. 2020;7:1736742.

47. Duan Y, Guo D, Zhang X, et al. Diagnostic accuracy of optical coherence tomography for margin assessment in breast-conserving surgery: a systematic review and meta-analysis. Photodiagnosis Photodyn Ther. 2023;43:103718.

48. Fan S, Zhang H, Meng Z, Li A, Luo Y, Liu Y. Comparing the diagnostic efficacy of optical coherence tomography and frozen section for margin assessment in breast-conserving surgery: a meta-analysis. J Clin Pathol. 2024;77:517-27.

49. Ramalhinho J, Bulathsinhala S, Gurusamy K, Davidson BR, Clarkson MJ. Assessing augmented reality displays in laparoscopic liver surgery - a clinical experience. Surg Endosc. 2025;39:5863-71.

50. Roman J, Sengul I, Němec M, et al. Augmented and mixed reality in liver surgery: a comprehensive narrative review of novel clinical implications on cohort studies. Rev Assoc Med Bras. 2025;71:e20250315.

51. Raissi-dehkordi N, Raissi-dehkordi N, Xu B. Contemporary applications of artificial intelligence and machine learning in echocardiography. npj Cardiovasc Health. 2025;2:64.

52. Hirata Y, Kusunose K. AI in echocardiography: state-of-the-art automated measurement techniques and clinical applications. JMA J. 2025;8:141-50.

53. Wagner M, Müller-Stich BP, Kisilenko A, et al. Comparative validation of machine learning algorithms for surgical workflow and skill analysis with the HeiChole benchmark. Med Image Anal. 2023;86:102770.

54. Lavanchy JL, Ramesh S, Dall’Alba D, et al. Challenges in multi-centric generalization: phase and step recognition in Roux-en-Y gastric bypass surgery. Int J Comput Assist Radiol Surg. 2024;19:2249-57.

55. Abdelwanis M, Alarafati HK, Tammam MMS, Simsekler MCE. Exploring the risks of automation bias in healthcare artificial intelligence applications: a Bowtie analysis. Journal of Safety Science and Resilience. 2024;5:460-9.

56. Tun HM, Rahman HA, Naing L, Malik OA. Trust in artificial intelligence-based clinical decision support systems among health care workers: systematic review. J Med Internet Res. 2025;27:e69678.

57. Sadeghi Z, Alizadehsani R, Cifci MA, et al. A review of Explainable Artificial Intelligence in healthcare. Comput Electr Eng. 2024;118:109370.

58. GOV.UK. Equity in medical devices: independent review - final report. Available from: https://www.gov.uk/government/publications/equity-in-medical-devices-independent-review-final-report [accessed 16 December 2025].

59. European Commission. MDCG 2025-6 - FAQ on interplay between the medical devices regulation & in vitro diagnostic medical devices regulation and the artificial intelligence act (June 2025). Available from: https://health.ec.europa.eu/latest-updates/mdcg-2025-6-faq-interplay-between-medical-devices-regulation-vitro-diagnostic-medical-devices-2025-06-19_en [accessed 16 December 2025].

60. US Food and Drug Administration. Marketing submission recommendations for a predetermined change control plan for artificial intelligence-enabled device software functions. Available from: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/marketing-submission-recommendations-predetermined-change-control-plan-artificial-intelligence [accessed 16 December 2025].

61. US Food and Drug Administration. Good machine learning practice for medical device development: guiding principles. Available from: https://www.fda.gov/medical-devices/software-medical-device-samd/good-machine-learning-practice-medical-device-development-guiding-principles [accessed 16 December 2025].

62. International Medical Device Regulations Forum. Good machine learning practice for medical device development: guiding principles. AUTHORING GROUP: Artificial Intelligence/Machine Learning-enabled Working Group, 2025. Available from: https://www.imdrf.org/sites/default/files/2025-01/IMDRF_AIML%20WG_GMLP_N88%20Final_0.pdf [accessed 16 December 2025].