Skip to main content
SpringerLink
Log in

Artificial Intelligence in the Management of Difficult Decisions in Surgery and Operating Room Optimization

  • Chapter
  • First Online:
The High-risk Surgical Patient

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) algorithms have several applications in everyday life and many scientific fields. Relatively recent in medicine physical and virtual use of AI implements classifications, diagnoses, therapies, and follow-ups. AI has been defined as the study of algorithms that give machines the ability to reason and perform functions, such as problem solving, object and word recognition, and decision making. Predicting outcomes from various features or finding recurring patterns within multidimensional data sets are the tasks of ML in medicine. There are three types of ML algorithms: Supervised, Unsupervised, and Reinforcement Learning. The most common applications include disease diagnosis, health monitoring and digital patient consultations, clinical training, patient data management, drug development, and personalized medicine.

ML approaches are able to support decisions in critically ill patients improving the quality of care in the Intensive Care Unit. Perioperative period and operating room logistics are also optimized by AI technologies. Some meaningful examples are reported in the text.

Medical education will necessarily contain concepts regarding AI and ML because they are present and future states in clinical work. In order to encourage multidisciplinary collaborations, international laws and politics should be adopted. AI is a valuable tool for clinicians; however, ethical, cultural, and societal implications are not profoundly cleared.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Connor CW. Artificial intelligence and machine learning in anesthesiology. Anesthesiology. 2019;131:1346–59.

    Article  PubMed  Google Scholar 

  2. Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol. 2019;28:73–81.

    Article  PubMed  Google Scholar 

  3. Handelman GS, Kok HK, Chandra RV, Razavi AH, Lee MJ, Asadi H. eDoctor: machine learning and the future of medicine. J Intern Med. 2018;284:603–19.

    Article  CAS  PubMed  Google Scholar 

  4. Hashimoto DA, Witkowski E, Gao L, Meireles O, Rosman G. Artificial intelligence in anesthesiology: current techniques, clinical applications, and limitations. Anesthesiology. 2020;132:379–94.

    Article  PubMed  Google Scholar 

  5. Parums DV. Editorial: artificial intelligence (AI) in clinical medicine and the 2020 CONSORT-AI study guidelines. Med Sci Monit. 2021;27:e933675. https://doi.org/10.12659/MSM.933675.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017;69:S36–40.

    Article  CAS  Google Scholar 

  7. Deo RC. Machine learning in medicine. Circulation. 2015;132:1920–30.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Padmanabhan R, Meskin N, Haddad WM. Closed-loop control of anesthesia and mean arterial pressure using reinforcement learning. Biomed Signal Process Control. 2015;22:54–64.

    Article  Google Scholar 

  9. Ramesh AN, Kambhampati C, Monson JRT, Drew PJ. Artificial intelligence in medicine. Ann R Coll Surg Engl. 2004;86:334–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Sinsky C, Colligan L, Li L, Prgomet M, Reynolds S, Goeders L, Westbrook J, Tutty M, Blike G. Allocation of physician time in ambulatory practice: a time and motion study in 4 specialties. Ann Intern Med. 2016;165:753–60.

    Article  PubMed  Google Scholar 

  11. Ansari S, Belle A, Ghanbari H, Salamango M, Najarian K. Suppression of false arrhythmia alarms in the ICU: a machine learning approach. Physiol Meas. 2016;37:1186–203.

    Article  PubMed  Google Scholar 

  12. Eerikainen LM, Vanschoren J, Rooijakkers MJ, Vullings R, Aarts RM. Reduction of false arrhythmia alarms using signal selection and machine learning. Physiol Meas. 2016;37:1204–16.

    Article  PubMed  Google Scholar 

  13. Antink CH, Leonhardt S, Walter M. Reducing false alarms in the ICU by quantifying self-similarity of multimodal biosignals. Physiol Meas. 2016;37:1233–52.

    Article  PubMed  Google Scholar 

  14. Ostojic D, Guglielmini S, Moser V, Fauchère JC, Bucher HU, Bassler D, Wolf M, Kleiser S, Scholkmann F, Karen T. Reducing false alarm rates in neonatal intensive care: a new machine learning approach. Adv Exp Med Biol. 2020;1232:285–90.

    Article  CAS  PubMed  Google Scholar 

  15. Tuwatananurak JP, Zadeh S, Xu X, Vacanti JA, Fulton WR, Ehrenfeld JM, Urman RD. Machine learning can improve estimation of surgical case duration: a pilot study. J Med Syst. 2019;43(3):44. https://doi.org/10.1007/s10916-019-1160-5.

    Article  PubMed  Google Scholar 

  16. Fairley M, Scheinker D, Brandeau ML. Improving the efficiency of the operating room environment with an optimization and machine learning model. Health Care Manag Sci. 2019;22:756–67.

    Article  PubMed  Google Scholar 

  17. Bose S, Talmor D. Who is a high-risk surgical patient? Curr Opin Crit Care. 2018;24:547–53.

    Article  PubMed  Google Scholar 

  18. Moustafa MA, El-Metainy S, Mahar K, Mahmoud Abdel-magied E. Defining difficult laryngoscopy findings by using multiple parameters: a machine learning approach. Egypt J Anaesth. 2017;33:153–8.

    Article  Google Scholar 

  19. Lao J, Chen Y, Li Z-C, Li Q, Zhang J, Liu J, Zhai G. A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme. Sci Rep. 2017;7(1):10353. https://doi.org/10.1038/S41598-017-10649-8.

    Article  PubMed  PubMed Central  Google Scholar 

  20. van Timmeren JE, Cester D, Tanadini-Lang S, Alkadhi H, Baessler B. Radiomics in medical imaging—“how-to” guide and critical reflection. Insights into Imaging. 2020;111(11):1–16.

    Google Scholar 

  21. Cysouw MCF, Jansen BHE, van de Brug T, Oprea-Lager DE, Pfaehler E, de Vries BM, van Moorselaar RJA, Hoekstra OS, Vis AN, Boellaard R. Machine learning-based analysis of [18F]DCFPyL PET radiomics for risk stratification in primary prostate cancer. Eur J Nucl Med Mol Imaging. 2021;48:340–9.

    Article  CAS  PubMed  Google Scholar 

  22. Lötsch J, Ultsch A, Kalso E. Prediction of persistent post-surgery pain by preoperative cold pain sensitivity: Biomarker development with machine-learning-derived analysis. Br J Anaesth. 2017;119:821–9.

    Article  PubMed  Google Scholar 

  23. Verdonck M, Carvalho H, Berghmans J, Forget P, Poelaert J. Exploratory outlier detection for acceleromyographic neuromuscular monitoring: machine learning approach. J Med Internet Res. 2021;23(6):e25913. https://doi.org/10.2196/25913.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Jeddah D, Chen O, Lipsky AM, Forgacs A, Celniker G, Lilly CM, Pessach IM. Validation of an automatic tagging system for identifying respiratory and hemodynamic deterioration events in the intensive care unit. Healthc Inform Res. 2021;27:241–8.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Pirracchio R, Cohen MJ, Malenica I, Cohen J, Chambaz A, Cannesson M, Lee C, Resche-Rigon M, Hubbard A. Big data and targeted machine learning in action to assist medical decision in the ICU. Anaesth Crit Care Pain Med. 2019;38:377–84.

    Article  PubMed  Google Scholar 

  26. Pai K-C, Wang M-S, Chen Y-F, Tseng C-H, Liu P-Y, Chen L-C, Sheu R-K, Chieh-Liang W. An artificial intelligence approach to bloodstream infections prediction. J Clin Med. 2021;10:2901.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Miao W, Xianjin D, Raymond G, Wei J. Artificial intelligence for clinical decision support in sepsis. Front Med. 2021;8:665464. https://doi.org/10.3389/FMED.2021.665464.

    Article  Google Scholar 

  28. Peiffer-Smadja N, Rawson TM, Ahmad R, Buchard A, Pantelis G, Lescure FX, Birgand G, Holmes AH. Machine learning for clinical decision support in infectious diseases: a narrative review of current applications. Clin Microbiol Infect. 2020;26:584–95.

    Article  CAS  PubMed  Google Scholar 

  29. Zeng Z, Yao S, Zheng J, Gong X. Development and validation of a novel blending machine learning model for hospital mortality prediction in ICU patients with sepsis. BioData Min. 2021;14(1):40. https://doi.org/10.1186/S13040-021-00276-5.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Lee H-C, Yoon H-K, Nam K, Cho Y, Kim T, Kim W, Bahk J-H. Derivation and validation of machine learning approaches to predict acute kidney injury after cardiac surgery. J Clin Med. 2018;7:322.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Murphy K, Smits H, Knoops AJG, et al. COVID-19 on chest radiographs: a multireader evaluation of an artificial intelligence system. Radiology. 2020;296:E166–72.

    Article  PubMed  Google Scholar 

  32. Mei X, Lee H-C, Diao K-Y, et al. Artificial intelligence-enabled rapid diagnosis of patients with COVID-19. Nat Med. 2020;26:1224–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Jiang L, Zhe W, Xiaolan X, Zhan Y, Jin X, Wang L, Qiu Y. Opportunities and challenges of artificial intelligence in the medical field: current application, emerging problems, and problem-solving strategies. J Int Med Res. 2021;49(3):3000605211000157. https://doi.org/10.1177/03000605211000157.

    Article  PubMed  Google Scholar 

  34. Yujin O, Park S, Ye JC. Deep learning COVID-19 features on CXR using limited training data sets. IEEE Trans Med Imaging. 2020;39:2688–700.

    Article  Google Scholar 

  35. Bragazzi NL, Dai H, Damiani G, Behzadifar M, Martini M, Wu J. How big data and artificial intelligence can help better manage the COVID-19 pandemic. Int J Environ Res Public Health. 2020;17(9):3176. https://doi.org/10.3390/IJERPH17093176.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Bellini V, Montomoli J, Bignami E. Poor quality data, privacy, lack of certifications: the lethal triad of new technologies in intensive care. Intensive Care Med. 2021;47(9):1052–3. https://doi.org/10.1007/S00134-021-06473-4.

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, Italy

    Elena Bignami, Valentina Bellini & Emanuele Paolo Rafano Carnà

Authors
  1. Elena Bignami
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Valentina Bellini
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Emanuele Paolo Rafano Carnà
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Elena Bignami .

Editor information

Editors and Affiliations

  1. Dipartimento di Emergenza Urgenza, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy

    Paolo Aseni

  2. Divisione di Cardiochirurgia, IRCCS Fondazione Policlinico San Matteo, Pavia, Pavia, Italy

    Antonino Massimiliano Grande

  3. Div. of Emerg. Surg., Abdominal Center, Helsinki University Hospital Meilahti, Helsinki, Finland

    Ari Leppäniemi

  4. Department of Pathophysiology and Transplantation, General Surgery and Trauma Team, University of Milano, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy

    Osvaldo Chiara

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Bignami, E., Bellini, V., Carnà, E.P.R. (2023). Artificial Intelligence in the Management of Difficult Decisions in Surgery and Operating Room Optimization. In: Aseni, P., Grande, A.M., Leppäniemi, A., Chiara, O. (eds) The High-risk Surgical Patient. Springer, Cham. https://doi.org/10.1007/978-3-031-17273-1_59

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-17273-1_59

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-17272-4

  • Online ISBN: 978-3-031-17273-1

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation