Skip to main content
SpringerLink
Log in

Artificial Intelligence in Surgery

  • Living reference work entry
  • First Online:
Artificial Intelligence in Medicine

  • 100 Accesses

Abstract

Artificial intelligence (AI) is the study of algorithms that give machines the ability to reason and perform cognitive functions. Applications of AI in medicine broadly and surgery, more specifically, have grown over the last few years as technology has advanced and clinical data has become more digitally accessible. It is becoming more important for surgeons to develop a fundamental understanding of the common techniques, applications, limitations, and ethical considerations of AI in surgery. This chapter provides an overview of AI for surgeons and describes ways in which surgeons can play a role in future development of AI applications.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

References

  1. Bellman R. An introduction to artificial intelligence: can computers think? Thomson Course Technology; 1978.

    Google Scholar 

  2. Administration USF & D, U.S. Food & Drug Administration. FDA permits marketing of artificial intelligence-based device to detect certain diabetes-related eye problems. Case Med Res. 2018. https://doi.org/10.31525/fda2-ucm604357.htm.

  3. Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial intelligence in surgery: promises and perils. Ann Surg. 2018;268(1):70–6.

    Article  Google Scholar 

  4. Hashimoto DA, Witkowski E, Gao L, Meireles O, Rosman G. Artificial intelligence in anesthesiology. Anesthesiology. 2019;132:379. https://doi.org/10.1097/aln.0000000000002960.

    Article  Google Scholar 

  5. Avanzolini G, Barbini P, Gnudi G. Unsupervised learning and discriminant analysis applied to identification of high risk postoperative cardiac patients. Int J Biomed Comput. 1990;25(2–3):207–21. https://doi.org/10.1016/0020-7101(90)90010-r.

    Article  CAS  Google Scholar 

  6. DiPietro R, Hager GD. Unsupervised learning for surgical motion by learning to predict the future, vol. 2018. Medical Image Computing and Computer Assisted Intervention – MICCAI; 2018. p. 281–8. https://doi.org/10.1007/978-3-030-00937-3_33.

    Book  Google Scholar 

  7. Skinner BF. The behavior of organisms: an experimental analysis. B. F. Skinner Foundation; 1990.

    Google Scholar 

  8. Silver D, Schrittwieser J, Simonyan K, et al. Mastering the game of go without human knowledge. Nature. 2017;550(7676):354–9.

    Article  CAS  Google Scholar 

  9. Hebb DO. The organization of behavior. Taylor and Francis; 2005. https://doi.org/10.4324/9781410612403.

    Book  Google Scholar 

  10. Natarajan P, Frenzel JC, Smaltz DH. Demystifying big data and machine learning for healthcare. Taylor and Francis; 2017. https://doi.org/10.1201/9781315389325.

    Book  Google Scholar 

  11. Esteva A, Robicquet A, Ramsundar B, et al. A guide to deep learning in healthcare. Nat Med. 2019;25(1):24–9.

    Article  CAS  Google Scholar 

  12. Grzybowski A, Brona P, Lim G, et al. Artificial intelligence for diabetic retinopathy screening: a review. Eye. 2019. https://doi.org/10.1038/s41433-019-0566-0.

  13. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115–8.

    Article  CAS  PubMed  Google Scholar 

  14. Rodas NL, Padoy N. Augmented reality for reducing intraoperative radiation exposure to patients and clinicians during x-ray guided procedures. In: Mixed and augmented reality in medicine. CRC Press; 2018. p. 217–29. https://doi.org/10.1201/9781315157702-15.

    Chapter  Google Scholar 

  15. Nadkarni PM, Ohno-Machado L, Chapman WW. Natural language processing: an introduction. J Am Med Inform Assoc. 2011;18(5):544–51.

    Article  PubMed  Google Scholar 

  16. Hughes KS, Zhou J, Bao Y, Singh P, Wang J, Yin K. Natural language processing to facilitate breast cancer research and management. Breast J. 2020;26(1):92–9.

    Article  Google Scholar 

  17. Zunic A, Corcoran P, Spasic I. Sentiment analysis in health and well-being: systematic review. JMIR Med Inform. 2020;8(1):e16023.

    Article  PubMed  Google Scholar 

  18. Shen F, Larson DW, Naessens JM, Habermann EB, Liu H, Sohn S. Detection of surgical site infection utilizing automated feature generation in clinical notes. Int J Healthc Inf Syst Inform. 2019;3(3):267–82.

    Google Scholar 

  19. Healey MA, Shackford SR, Osler TM, Rogers FB, Burns E. Complications in surgical patients. Arch Surg. 2002;137(5):611–7; discussion 617–618.

    Article  Google Scholar 

  20. Lee TH, Marcantonio ER, Mangione CM, et al. Derivation and prospective validation of a simple index for prediction of cardiac risk of major noncardiac surgery. Circulation. 1999;100(10):1043–9.

    Article  CAS  Google Scholar 

  21. Gupta PK, Gupta H, Sundaram A, et al. Development and validation of a risk calculator for prediction of cardiac risk after surgery. Circulation. 2011;124(4):381–7.

    Article  Google Scholar 

  22. POISE Study Group, Devereaux PJ, Yang H, et al. Effects of extended-release metoprolol succinate in patients undergoing non-cardiac surgery (POISE trial): a randomised controlled trial. Lancet. 2008;371(9627):1839–47.

    Article  Google Scholar 

  23. Wijeysundera DN, Pearse RM, Shulman MA, et al. Assessment of functional capacity before major non-cardiac surgery: an international, prospective cohort study. Lancet. 2018;391(10140):2631–40.

    Article  Google Scholar 

  24. Wolters U, Wolf T, Stützer H, Schröder T. ASA classification and perioperative variables as predictors of postoperative outcome. Br J Anaesth. 1996;77(2):217–22.

    Article  CAS  Google Scholar 

  25. Owens WD, Felts JA, Spitznagel EL Jr. ASA physical status classifications: a study of consistency of ratings. Anesthesiology. 1978;49(4):239–43.

    Article  CAS  Google Scholar 

  26. Bilimoria KY, Liu Y, Paruch JL, et al. Development and evaluation of the universal ACS NSQIP surgical risk calculator: a decision aid and informed consent tool for patients and surgeons. J Am Coll Surg. 2013;217(5):833–42.e1–e3.

    Article  PubMed  Google Scholar 

  27. Corey KM, Kashyap S, Lorenzi E, et al. Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): a retrospective, single-site study. PLoS Med. 2018;15(11):e1002701.

    Article  PubMed  Google Scholar 

  28. Bihorac A, Ozrazgat-Baslanti T, Ebadi A, et al. MySurgeryRisk: development and validation of a machine-learning risk algorithm for major complications and death after surgery. Ann Surg. 2019;269(4):652–62.

    Article  Google Scholar 

  29. Bertsimas D, Dunn J, Velmahos GC, Kaafarani HMA. Surgical risk is not linear. Ann Surg. 2018;268(4):574–83. https://doi.org/10.1097/sla.0000000000002956.

    Article  Google Scholar 

  30. Birkmeyer JD, Stukel TA, Siewers AE, Goodney PP, Wennberg DE, Lucas FL. Surgeon volume and operative mortality in the United States. ACC Curr J Rev. 2004;13(2):59. https://doi.org/10.1016/j.accreview.2003.12.065.

    Article  Google Scholar 

  31. Birkmeyer JD, Finks JF, O’Reilly A, et al. Surgical skill and complication rates after bariatric surgery. N Engl J Med. 2013;369(15):1434–42.

    Article  CAS  Google Scholar 

  32. Bonrath EM, Gordon LE, Grantcharov TP. Characterising “near miss” events in complex laparoscopic surgery through video analysis. BMJ Qual Saf. 2015;24(8):516–21. https://doi.org/10.1136/bmjqs-2014-003816.

    Article  Google Scholar 

  33. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM. 2017;60(6):84–90. https://doi.org/10.1145/3065386.

    Article  Google Scholar 

  34. Hashimoto DA, Rosman G, Witkowski ER, et al. Computer vision analysis of intraoperative video: automated recognition of operative steps in laparoscopic sleeve gastrectomy. Ann Surg. 2019;270(3):414–21.

    Article  Google Scholar 

  35. Kannan S, Yengera G, Mutter D, Marescaux J, Padoy N. Future-state predicting LSTM for early surgery type recognition. IEEE Trans Med Imaging. 2019. https://doi.org/10.1109/TMI.2019.2931158.

  36. 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. 2019. https://doi.org/10.1007/s00464-019-07281-0.

  37. Twinanda AP, Yengera G, Mutter D, Marescaux J, Padoy N. RSDNet: learning to predict remaining surgery duration from laparoscopic videos without manual annotations. IEEE Trans Med Imaging. 2019;38(4):1069–78.

    Article  Google Scholar 

  38. Mascagni P, Fiorillo C, Urade T, et al. Formalizing video documentation of the Critical View of Safety in laparoscopic cholecystectomy: a step towards artificial intelligence assistance to improve surgical safety. Surg Endosc. 2019. https://doi.org/10.1007/s00464-019-07149-3.

Download references

Author information

Authors and Affiliations

  1. Intraoperative Performance Analytics Laboratory, Department of Surgery, Lenox Hill Hospital, Hofstra School of Medicine at Northwell, New York, NY, USA

    Filippo Filicori

  2. Surgical Artificial Intelligence and Innovation Laboratory, Department of Surgery, Massachusetts General Hospital, Boston, MA, USA

    Ozanan R. Meireles

Authors
  1. Filippo Filicori
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ozanan R. Meireles
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ozanan R. Meireles .

Editor information

Editors and Affiliations

  1. Karolinska Institute, Stockholm, Sweden

    Niklas Lidströmer

  2. Faculty of Medicine, Imperial College London, London, UK

    Hutan Ashrafian

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Filicori, F., Meireles, O.R. (2021). Artificial Intelligence in Surgery. In: Lidströmer, N., Ashrafian, H. (eds) Artificial Intelligence in Medicine. Springer, Cham. https://doi.org/10.1007/978-3-030-58080-3_171-1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-58080-3_171-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58080-3

  • Online ISBN: 978-3-030-58080-3

  • eBook Packages: Springer Reference MedicineReference Module Medicine

Publish with us

Policies and ethics

Search

Navigation