AI-Powered Innovations in Digestive Surgery: Current Evidence and Future Perspectives - A Systematic Literature

Authors

  • Rudi Pandapotan Napitupulu RSPAL dr Ramelan Surabaya
  • Ghaly Khaidar Ardli RSPAL dr Ramelan Surabaya
  • Muhammad Rizky Ramadhani Klinik Umum Pratama Jaya Husada
  • Neysa Ornella Dira Rumah Sakit Islam PKU Muhammadiyah Pekajangan
  • Andini Kartika Sari Klinik Assyifa

DOI:

https://doi.org/10.58344/ihj.v4i1.677

Keywords:

artificial intelligence, digestive surgery, machine learning, perioperative management

Abstract

Artificial Intelligence (AI) is transforming the field of digestive surgery, offering significant advancements in diagnostic accuracy, surgical precision, and perioperative management. Technologies such as machine learning (ML) and deep learning (DL) enhance surgical outcomes by identifying complex patterns in data, supporting real-time decision-making, and improving personalized patient care. This study aims to systematically evaluate the current applications, challenges, and future potential of AI-powered innovations in digestive surgery. A systematic literature review was conducted following PRISMA guidelines. The search utilized databases including Scopus, PubMed, Google Scholar, Crossref, and Web of Science. Keywords like "artificial intelligence," "machine learning," "digestive surgery," and "surgical AI" yielded 3,285 articles. After applying inclusion criteria, 23 studies published between 2020 and 2024 were selected for narrative synthesis. The review highlights AI's applications in endoscopic procedures, achieving diagnostic accuracy up to 98% for early-stage cancers. AI-driven systems enhance intraoperative precision, particularly in laparoscopic and robotic surgeries, reducing complication risks and improving outcomes. Moreover, AI-based predictive models support perioperative management by optimizing surgical planning and postoperative care, demonstrating improved efficiency and patient safety. AI is reshaping digestive surgery by enabling better diagnostics, personalized treatment, and improved surgical outcomes. Despite challenges like data quality and ethical concerns, the continued evolution of AI technologies holds promise for further advancements in surgical practices and education

References

Bari, H., Wadhwani, S., & Dasari, B. V. M. (2021). Role of artificial intelligence in hepatobiliary and pancreatic surgery. World Journal of Gastrointestinal Surgery, 13(1), 7. https://doi.org/10.4240/wjgs.v13.i1.7

Bekta?, M., Reiber, B. M. M., Pereira, J. C., Burchell, G. L., & van der Peet, D. L. (2022). Artificial intelligence in bariatric surgery: current status and future perspectives. Obesity Surgery, 32(8), 2772–2783. https://doi.org/10.1007/s11695-022-06146-1

Binkley, C. E., & Green, B. P. (2021). Does intraoperative artificial intelligence decision support pose ethical issues? JAMA Surgery, 156(9), 809–810. https://doi.org/10.1001/jamasurg.2021.2055

Bodenstedt, S., Wagner, M., Müller-Stich, B. P., Weitz, J., & Speidel, S. (2020). Artificial intelligence-assisted surgery: potential and challenges. Visceral Medicine, 36(6), 450–455. https://doi.org/10.1159/000511351

Busnatu, ?tefan, Niculescu, A.-G., Bolocan, A., Petrescu, G. E. D., P?duraru, D. N., N?stas?, I., Lupu?oru, M., Geant?, M., Andronic, O., & Grumezescu, A. M. (2022). Clinical applications of artificial intelligence—an updated overview. Journal of Clinical Medicine, 11(8), 2265. https://doi.org/10.3390/jcm11082265

Cai, Y.-W., Dong, F.-F., Shi, Y.-H., Lu, L.-Y., Chen, C., Lin, P., Xue, Y.-S., Chen, J.-H., Chen, S.-Y., & Luo, X.-B. (2021). Deep learning driven colorectal lesion detection in gastrointestinal endoscopic and pathological imaging. World Journal of Clinical Cases, 9(31), 9376. https://doi.org/10.12998/wjcc.v9.i31.9376

Cao, Y., Näslund, I., Näslund, E., Ottosson, J., Montgomery, S., & Stenberg, E. (2021). Using a convolutional neural network to predict remission of diabetes after gastric bypass surgery: machine learning study from the scandinavian obesity surgery register. JMIR Medical Informatics, 9(8), e25612. https://doi.org/10.1101/2020.11.03.20224956

Chaibi, A., & Zaiem, I. (2022). Doctor resistance of artificial intelligence in healthcare. International Journal of Healthcare Information Systems and Informatics (IJHISI), 17(1), 1–13. https://doi.org/10.4018/ijhisi.315618

Chen, H., Liu, S., Huang, S., Liu, M., & Chen, G. (2024). Applications of artificial intelligence in gastroscopy: a narrative review. Journal of International Medical Research, 52(1), 03000605231223454. https://doi.org/10.1177/03000605231223454

De Simone, B., Abu-Zidan, F. M., Gumbs, A. A., Chouillard, E., Di Saverio, S., Sartelli, M., Coccolini, F., Ansaloni, L., Collins, T., & Kluger, Y. (2022). Knowledge, attitude, and practice of artificial intelligence in emergency and trauma surgery, the ARIES project: an international web-based survey. World Journal of Emergency Surgery, 17(1), 10. https://doi.org/10.1186/s13017-022-00413-3.

Eltawil, F. A., Atalla, M., Boulos, E., Amirabadi, A., & Tyrrell, P. N. (2023). Analyzing barriers and enablers for the acceptance of artificial intelligence innovations into radiology practice: a scoping review. Tomography, 9(4), 1443–1455. https://doi.org/10.3390/tomography9040115

Emile, S. H., Ghareeb, W., Elfeki, H., El Sorogy, M., Fouad, A., & Elrefai, M. (2022). Development and validation of an artificial intelligence-based model to predict gastroesophageal reflux disease after sleeve gastrectomy. Obesity Surgery, 32(8), 2537–2547. https://doi.org/10.1007/s11695-022-06112-x

Endo, Y., Tokuyasu, T., Mori, Y., Asai, K., Umezawa, A., Kawamura, M., Fujinaga, A., Ejima, A., Kimura, M., & Inomata, M. (2023). Impact of AI system on recognition for anatomical landmarks related to reducing bile duct injury during laparoscopic cholecystectomy. Surgical Endoscopy, 37(7), 5752–5759. https://doi.org/10.1007/s00464-023-10224-5

Feng, Z., Sun, Z., Zhang, Q., & Ren, S. (2023). Analysis of clinical efficacy and safety of hand-sewn anastomosis for the digestive tract with Da Vinci robot in rectal cancer surgery. World Journal of Surgical Oncology, 21(1), 317. https://doi.org/10.1186/s12957-023-03172-w

Giovanardi, F. (2020). Robotic distal subtotal gastrectomy with D2 lymphadenectomy for advanced gastric cancer: A case report and technical description. Journal of Gastric Surgery, 2(1), 18–21. https://doi.org/10.36159/jgs.v2i1.22

Golany, T., Aides, A., Freedman, D., Rabani, N., Liu, Y., Rivlin, E., Corrado, G. S., Matias, Y., Khoury, W., & Kashtan, H. (2022). Artificial intelligence for phase recognition in complex laparoscopic cholecystectomy. Surgical Endoscopy, 36(12), 9215–9223. https://doi.org/10.1007/s00464-022-09405-5

Goodman, E. D., Patel, K. K., Zhang, Y., Locke, W., Kennedy, C. J., Mehrotra, R., Ren, S., Guan, M. Y., Downing, M., & Chen, H. W. (2021). A real-time spatiotemporal AI model analyzes skill in open surgical videos. ArXiv Preprint ArXiv:2112.07219. https://doi.org/10.48550/arxiv.2112.07219

Gruetzemacher, R., & Whittlestone, J. (2022). The transformative potential of artificial intelligence. Futures, 135, 102884. https://doi.org/10.1016/j.futures.2021.102884

Guerrero, D. T., Asaad, M., Rajesh, A., Hassan, A., & Butler, C. E. (2023). Advancing surgical education: the use of artificial intelligence in surgical training. The American Surgeon, 89(1), 49–54. https://doi.org/10.1177/00031348221101503

Hamilton, A. (2024). The future of artificial intelligence in surgery. Cureus, 16(7). https://doi.org/10.7759/cureus.63699

Hsiao, Y.J. et al. (2021) ‘Application of Artificial Intelligence-Driven Endoscopic Screening and Diagnosis of Gastric Cancer’, World Journal of Gastroenterology. https://doi.org/10.3748/wjg.v27.i22.2979.

Hassan, A. M., Rajesh, A., Asaad, M., Nelson, J. A., Coert, J. H., Mehrara, B. J., & Butler, C. E. (2023). A surgeon’s guide to artificial intelligence-driven predictive models. The American Surgeon, 89(1), 11–19. https://doi.org/10.1177/00031348221103648

Kitaguchi, D. et al. (2021) ‘Artificial Intelligence?based Computer Vision in Surgery: Recent Advances and Future Perspectives’, Annals of Gastroenterological Surgery. https://doi.org/10.1002/ags3.12513.

Kuemmerli, C. et al. (2023) ‘Artificial Intelligence in Pancreatic Surgery: Current Applications’, Journal of Pancreatology. https://doi.org/10.1097/jp9.0000000000000129.

Laï, M.-C., Brian, M., & Mamzer, M.-F. (2020). Perceptions of artificial intelligence in healthcare: findings from a qualitative survey study among actors in France. Journal of Translational Medicine, 18, 1–13. https://doi.org/10.1186/s12967-019-02204-y.

Larrain, C. (2024) ‘Artificial Intelligence, Machine Learning, and Deep Learning in the Diagnosis and Management of Hepatocellular Carcinoma’, Livers. https://doi.org/10.3390/livers4010004.

Melstrom, L.G. et al. (2020) ‘Patient Generated Health Data and Electronic Health Record Integration in Oncologic Surgery: A Call for Artificial Intelligence and Machine Learning’, Journal of Surgical Oncology. https://doi.org/10.1002/jso.26232.

Moglia, A. et al. (2022) ‘Ensemble Deep Learning for the Prediction of Proficiency at a Virtual Simulator for Robot-Assisted Surgery’, Surgical Endoscopy. https://doi.org/10.1007/s00464-021-08999-6.

Nema, S. and Vachhani, L. (2022) ‘Surgical Instrument Detection and Tracking Technologies: Automating Dataset Labeling for Surgical Skill Assessment’, Frontiers in Robotics and Ai. https://doi.org/10.3389/frobt.2022.1030846.

Nishigori, T., Ichihara, N., Obama, K., Uyama, I., Miyata, H., Inomata, M., Kakeji, Y., Kitagawa, Y., & Sakai, Y. (2022). Prevalence and safety of robotic surgery for gastrointestinal malignant tumors in Japan. Annals of Gastroenterological Surgery, 6(6), 746–752. https://doi.org/10.1002/ags3.12579

Ortenzi, M. (2023) ‘A Novel High Accuracy Model for Automatic Surgical Workflow Recognition Using Artificial Intelligence in Laparoscopic Totally Extraperitoneal Inguinal Hernia Repair (TEP)’, Surgical Endoscopy. https://doi.org/10.1007/s00464-023-10375-5.

Pecere, S. et al. (2021) ‘Applications of Artificial Intelligence for the Diagnosis of Gastrointestinal Diseases’, Diagnostics. https://doi.org/10.3390/diagnostics11091575.

Pereira, S.S., Guimarães, M. and Monteiro, M.P. (2023) ‘Towards Precision Medicine in Bariatric Surgery Prescription’, Reviews in Endocrine and Metabolic Disorders. https://doi.org/10.1007/s11154-023-09801-9.

Parthasarathy, P. R., Patil, S. R., Dawasaz, A. A., Baig, F. A. H., Karobari, M. I., RP, P., Patil, S. R., & Baig, F. (2024). Unlocking the potential: investigating dental practitioners’ willingness to embrace artificial intelligence in dental practice. Cureus, 16(2). https://doi.org/10.7759/cureus.55107

Pesi, B., Bencini, L., Moraldi, L., Tofani, F., Batignani, G., Bechi, P., Farsi, M., Annecchiarico, M., & Coratti, A. (2021). Robotic versus open liver resection in hepatocarcinoma: surgical and oncological outcomes. Surgical Laparoscopy Endoscopy & Percutaneous Techniques, 31(4), 468–474. https://doi.org/10.1097/sle.0000000000000904

Quero, G. et al. (2022) ‘Artificial Intelligence in Colorectal Cancer Surgery: Present and Future Perspectives’, Cancers. https://doi.org/10.3390/cancers14153803.

Ramírez, J. G. C. (2024). AI in healthcare: Revolutionizing patient care with predictive analytics and decision support systems. Journal of Artificial Intelligence General Science (JAIGS) ISSN, 3006–4023. https://doi.org/10.60087/jaigs.v1i1.p37

Ravenel, M., Joliat, G.-R., Demartines, N., Uldry, E., Melloul, E., & Labgaa, I. (2023). Machine learning to predict postoperative complications after digestive surgery: a scoping review. British Journal of Surgery, 110(12), 1646–1649. https://doi.org/doi.org/10.1093/bjs/znad229

Reza, T., & Bokhari, S. F. H. (2024). Partnering with technology: advancing laparoscopy with artificial intelligence and machine learning. Cureus, 16(3). https://doi.org/10.7759/cureus.56076

Ryu, S., Goto, K., Imaizumi, Y., & Nakabayashi, Y. (2024). Laparoscopic colorectal surgery with anatomical recognition with artificial intelligence assistance for nerves and dissection layers. Annals of Surgical Oncology, 31(3), 1690–1691. https://doi.org/10.48550/arxiv.2112.07219

Spinelli, A., Carrano, F. M., Laino, M. E., Andreozzi, M., Koleth, G., Hassan, C., Repici, A., Chand, M., Savevski, V., & Pellino, G. (2023). Artificial intelligence in colorectal surgery: an AI-powered systematic review. Techniques in Coloproctology, 27(8), 615–629. https://doi.org/10.1007/s10151-023-02772-8

Stalder, A., Mazzola, F., Adamina, M., & Fahrner, R. (2024). The distribution of robotic surgery in general and visceral surgery departments in Switzerland–a nationwide inquiry. Innovative Surgical Sciences, 9(1), 55–62. https://doi.org/10.1515/iss-2023-0052

Strong, J.S. (2024) ‘Evaluating Surgical Expertise With AI?based Automated Instrument Recognition for Robotic Distal Gastrectomy’, Annals of Gastroenterological Surgery. https://doi.org/10.1002/ags3.12784.

Taha, A., Enodien, B., Frey, D. M., & Taha-Mehlitz, S. (2022). The development of artificial intelligence in hernia surgery: a scoping review. Frontiers in Surgery, 9, 908014. https://doi.org/10.3389/fsurg.2022.908014

Tranter?Entwistle, I. et al. (2020) ‘The Challenges of Implementing Artificial Intelligence Into Surgical Practice’, World Journal of Surgery. https://doi.org/10.1007/s00268-020-05820-8.

Tariq, A., Gill, A. Y., & Hussain, H. K. (2023). Evaluating the potential of artificial intelligence in orthopedic surgery for value-based healthcare. International Journal of Multidisciplinary Sciences and Arts, 2(2), 27–35. https://doi.org/10.47709/ijmdsa.v2i1.2394

Umer, F., Zeeshan, S., & Islam, S. (2024). Surgery in the digital era: Traversing advancements, challenges and future frontiers. https://doi.org/10.47391/jpma.aku-9s-01

Voskens, F. J., Abbing, J. R., Ruys, A. T., Ruurda, J. P., & Broeders, I. (2022). A nationwide survey on the perceptions of general surgeons on artificial intelligence. Artif Intell Surg, 2(1), 8–17. https://doi.org/10.20517/ais.2021.10

Xu, F.W.X. et al. (2023) ‘Augmenting Care in Hepatocellular Carcinoma With Artificial Intelligence’, Artificial Intelligence Surgery. https://doi.org/10.20517/ais.2022.33

Downloads

Published

2025-03-18