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Integration of AI in surgical decision support: improving clinical judgment

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Abstract

Though early in its development, artificial Intelligence (AI)-enabled clinical decision support (CDS) systems show promise in improving surgical decision-making. Applications for AI have been proposed in all surgical specialties and cover pre-operative risk assessment tools, intra-operative monitoring and decision-making, and post-operative patient management. These AI systems find patterns in data, with sources ranging from radiology, pathology, and intra-operative images to the human genome and real-time physiological parameters. They may improve hospital workflow through information extraction, documentation, and summarization. However, these new tools require validation in real-world clinical settings, adherence to standardized reporting guidelines, and a comprehensive evaluation of both performance and fairness metrics. In overcoming these limitations, AI is poised to offer data-driven insights to enhancing patient care.

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References

  1. Cresswell K, Callaghan M, Khan S, Sheikh Z, Mozaffar H, Sheikh A. Investigating the use of data-driven artificial intelligence in computerised decision support systems for health and social care: a systematic review. Health Informatics J. 2020;26(3):2138–47.

    Article  PubMed  Google Scholar 

  2. Moja L, Polo Friz H, Capobussi M, Kwag K, Banzi R, Ruggiero F, González-Lorenzo M, Liberati EG, Mangia M, Nyberg P, et al. Effectiveness of a hospital-based computerized decision support system on clinician recommendations and patient outcomes: a randomized clinical trial. JAMA Netw Open. 2019;2(12): e1917094.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Knoops PGM, Papaioannou A, Borghi A, Breakey RWF, Wilson AT, Jeelani O, Zafeiriou S, Steinbacher D, Padwa BL, Dunaway DJ, et al. A machine learning framework for automated diagnosis and computer-assisted planning in plastic and reconstructive surgery. Sci Rep. 2019;9(1):13597.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Taha-Mehlitz S, Däster S, Bach L, Ochs V, von Flüe M, Steinemann D, Taha A. Modern machine learning practices in colorectal surgery: a scoping review. J Clin Med. 2022;11.

  5. Solanki SL, Pandrowala S, Nayak A, Bhandare M, Ambulkar RP, Shrikhande SV. Artificial intelligence in perioperative management of major gastrointestinal surgeries. World J Gastroenterol. 2021;27(21):2758–70.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Stam WT, Goedknegt LK, Ingwersen EW, Schoonmade LJ, Bruns ERJ, Daams F. The prediction of surgical complications using artificial intelligence in patients undergoing major abdominal surgery: a systematic review. Surgery. 2022;171(4):1014–21.

    Article  PubMed  Google Scholar 

  7. Makhni EC, Makhni S, Ramkumar PN. Artificial intelligence for the orthopaedic surgeon: an overview of potential benefits, limitations, and clinical applications. J Am Acad Orthop Surg. 2021;29(6):235–43.

    Article  PubMed  Google Scholar 

  8. Tariciotti L, Palmisciano P, Giordano M, Remoli G, Lacorte E, Bertani G, Locatelli M, Dimeco F, Caccavella VM, Prada F. Artificial intelligence-enhanced intraoperative neurosurgical workflow: current knowledge and future perspectives. J Neurosurg Sci. 2022;66(2):139–50.

    Article  PubMed  Google Scholar 

  9. Seastedt KP, Moukheiber D, Mahindre SA, Thammineni C, Rosen DT, Watkins AA, Hashimoto DA, Hoang CD, Kpodonu J, Celi LA. A scoping review of artificial intelligence applications in thoracic surgery. Eur J Cardiothorac Surg. 2022;61(2):239–48.

    Article  PubMed  Google Scholar 

  10. Nedadur R, Wang B, Yanagawa B. The cardiac surgeon’s guide to artificial intelligence. Curr Opin Cardiol. 2021;36(5):637–43.

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  12. Zhou XY, Guo Y, Shen M, Yang GZ. Application of artificial intelligence in surgery. Front Med. 2020;14(4):417–30.

    Article  PubMed  Google Scholar 

  13. Bertsimas D, Dunn J, Velmahos GC, Kaafarani HMA. Surgical risk is not linear: derivation and validation of a novel, user-friendly, and machine-learning-based predictive optimal trees in emergency surgery risk (POTTER) calculator. Ann Surg. 2018;268(4):574–83.

    Article  PubMed  Google Scholar 

  14. Bihorac A, Ozrazgat-Baslanti T, Ebadi A, Motaei A, Madkour M, Pardalos PM, Lipori G, Hogan WR, Efron PA, Moore F, 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  PubMed  Google Scholar 

  15. Corey KM, Kashyap S, Lorenzi E, Lagoo-Deenadayalan SA, Heller K, Whalen K, Balu S, Heflin MT, McDonald SR, Swaminathan M, 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  PubMed Central  Google Scholar 

  16. Mahajan A, Esper S, Oo TH, McKibben J, Garver M, Artman J, Klahre C, Ryan J, Sadhasivam S, Holder-Murray J, et al. Development and validation of a machine learning model to identify patients before surgery at high risk for postoperative adverse events. JAMA Netw Open. 2023;6(7): e2322285.

    Article  PubMed  PubMed Central  Google Scholar 

  17. El Moheb M, Gebran A, Maurer LR, Naar L, El Hechi M, Breen K, Dorken-Gallastegi A, Sinyard R, Bertsimas D, Velmahos G, et al. Artificial intelligence versus surgeon gestalt in predicting risk of emergency general surgery. J Trauma Acute Care Surg. 2023;95(4):565–72.

    Article  PubMed  Google Scholar 

  18. Brennan M, Puri S, Ozrazgat-Baslanti T, Feng Z, Ruppert M, Hashemighouchani H, Momcilovic P, Li X, Wang DZ, Bihorac A. Comparing clinical judgment with the MySurgeryRisk algorithm for preoperative risk assessment: a pilot usability study. Surgery. 2019;165(5):1035–45.

    Article  PubMed  Google Scholar 

  19. Shaikh F, Dehmeshki J, Bisdas S, Roettger-Dupont D, Kubassova O, Aziz M, Awan O. Artificial intelligence-based clinical decision support systems using advanced medical imaging and radiomics. Curr Probl Diagn Radiol. 2021;50(2):262–7.

    Article  PubMed  Google Scholar 

  20. Tariciotti L, Caccavella VM, Fiore G, Schisano L, Carrabba G, Borsa S, Giordano M, Palmisciano P, Remoli G, Remore LG, et al. A deep learning model for preoperative differentiation of glioblastoma, brain metastasis and primary central nervous system lymphoma: a pilot study. Front Oncol 2022;12.

  21. Elhage SA, Deerenberg EB, Ayuso SA, Murphy KJ, Shao JM, Kercher KW, Smart NJ, Fischer JP, Augenstein VA, Colavita PD, et al. Development and validation of image-based deep learning models to predict surgical complexity and complications in abdominal wall reconstruction. JAMA Surg. 2021;156(10):933–40.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Azam MA, Sampieri C, Ioppi A, Benzi P, Giordano GG, De Vecchi M, Campagnari V, Li S, Guastini L, Paderno A, et al. Videomics of the upper aero-digestive tract cancer: deep learning applied to white light and narrow band imaging for automatic segmentation of endoscopic images. Front Oncol. 2022;12: 900451.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Sandbank J, Bataillon G, Nudelman A, Krasnitsky I, Mikulinsky R, Bien L, Thibault L, Albrecht Shach A, Sebag G, Clark DP, et al. Validation and real-world clinical application of an artificial intelligence algorithm for breast cancer detection in biopsies. NPJ Breast Cancer. 2022;8(1):129.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Marchetti MA, Cowen EA, Kurtansky NR, Weber J, Dauscher M, DeFazio J, Deng L, Dusza SW, Haliasos H, Halpern AC, et al. Prospective validation of dermoscopy-based open-source artificial intelligence for melanoma diagnosis (PROVE-AI study). NPJ Digital Med. 2023;6(1):127.

    Article  Google Scholar 

  25. Rakha EA, Toss M, Shiino S, Gamble P, Jaroensri R, Mermel CH, Chen PC. Current and future applications of artificial intelligence in pathology: a clinical perspective. J Clin Pathol. 2021;74(7):409–14.

    Article  CAS  PubMed  Google Scholar 

  26. Ayatollahi H, Hosseini SF, Hemmat M. Integrating genetic data into electronic health records: medical geneticists’ perspectives. Healthc Inform Res. 2019;25(4):289–96.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Navarrete-Welton AJ, Hashimoto DA. Current applications of artificial intelligence for intraoperative decision support in surgery. Front Med. 2020;14(4):369–81.

    Article  PubMed  Google Scholar 

  28. Gordon L, Grantcharov T, Rudzicz F. Explainable artificial intelligence for safe intraoperative decision support. JAMA Surg. 2019;154(11):1064–5.

    Article  PubMed  Google Scholar 

  29. Kennedy-Metz LR, Mascagni P, Torralba A, Dias RD, Perona P, Shah JA, Padoy N, Zenati MA. Computer vision in the operating room: opportunities and caveats. IEEE Trans Med Robot Bionics. 2021;3(1):2–10.

    Article  PubMed  Google Scholar 

  30. Lundberg SM, Nair B, Vavilala MS, Horibe M, Eisses MJ, Adams T, Liston DE, Low DK-W, Newman S-F, Kim J, et al. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nat Biomed Eng. 2018;2(10):749–60.

  31. Birkhoff DC, van Dalen A, Schijven MP. A review on the current applications of artificial intelligence in the operating room. Surg Innov. 2021;28(5):611–9.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Hashimoto DA, Rosman G, Witkowski ER, Stafford C, Navarette-Welton AJ, Rattner DW, Lillemoe KD, Rus DL, Meireles OR. Computer vision analysis of intraoperative video: automated recognition of operative steps in laparoscopic sleeve gastrectomy. Ann Surg. 2019;270(3):414–21.

    Article  PubMed  Google Scholar 

  33. Madani A, Namazi B, Altieri MS, Hashimoto DA, Rivera AM, Pucher PH, Navarrete-Welton A, Sankaranarayanan G, Brunt LM, Okrainec A, et al. Artificial intelligence for intraoperative guidance: using semantic segmentation to identify surgical anatomy during laparoscopic cholecystectomy. Ann Surg. 2022;276(2).

  34. Lam SSW, Zaribafzadeh H, Ang BY, Webster W, Buckland D, Mantyh C, Tan HK. Estimation of surgery durations using machine learning methods: a cross-country multi-site collaborative study. Healthcare (Basel). 2022;10(7).

  35. Igaki T, Kitaguchi D, Matsuzaki H, Nakajima K, Kojima S, Hasegawa H, Takeshita N, Kinugasa Y, Ito M. Automatic surgical skill assessment system based on concordance of standardized surgical field development using artificial intelligence. JAMA Surg. 2023;158(8): e231131.

    Article  PubMed  Google Scholar 

  36. Hashimoto DA, Rosman G, Meireles OR. Artificial intelligence in surgery: understanding the role of AI in surgical practice. McGraw-Hill Education; 2021.

  37. Lan L, Chen F, Luo J, Li M, Hao X, Hu Y, Yin J, Zhu T, Zhou X. Prediction of intensive care unit admission (>24 h) after surgery in elective noncardiac surgical patients using machine learning algorithms. Digit Health. 2022;8:20552076221110544.

    PubMed  PubMed Central  Google Scholar 

  38. Loftus TJ, Ruppert MM, Ozrazgat-Baslanti T, Balch JA, Efron PA, Tighe PJ, Hogan WR, Rashidi P, Upchurch GR Jr, Bihorac A. Association of postoperative undertriage to hospital wards with mortality and morbidity. JAMA Netw Open. 2021;4(11): e2131669.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Wang D, Carrano FM, Fisichella PM, Desiato V, Newman E, Berman R, Pachter HL, Melis M. A Quest for optimization of postoperative triage after major surgery. J Laparoendosc Adv Surg Tech A. 2019;29(2):203–5.

    Article  PubMed  Google Scholar 

  40. Datta S, Loftus TJ, Ruppert MM, Giordano C, Upchurch GR Jr, Rashidi P, Ozrazgat-Baslanti T, Bihorac A. Added value of intraoperative data for predicting postoperative complications: the MySurgeryRisk PostOp extension. J Surg Res. 2020;254:350–63.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Komorowski M, Celi LA, Badawi O, Gordon AC, Faisal AA. The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care. Nat Med. 2018;24(11):1716–20.

    Article  CAS  PubMed  Google Scholar 

  42. Knaus WA, Marks R. New phenotypes for sepsis: the promise and problem of applying machine learning and artificial intelligence in clinical research. JAMA. 2019.

  43. Wang J, Yang J, Zhang H, Lu H, Skreta M, Husić M, Arbabi A, Sultanum N, Brudno M. PhenoPad: building AI enabled note-taking interfaces for patient encounters. NPJ Digit Med. 2022;5(1):12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Yang X, Chen A, PourNejatian N, Shin HC, Smith KE, Parisien C, Compas C, Martin C, Costa AB, Flores MG, et al. A large language model for electronic health records. NPJ Digit Med. 2022;5(1):194.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Loftus TJ, Altieri MS, Balch JA, Abbott KL, Choi J, Marwaha JS, Hashimoto DA, Brat GA, Raftopoulos Y, Evans HL, et al. Artificial intelligence-enabled decision support in surgery: state-of-the-art and future directions. Ann Surg. 2023;278(1):51–8.

    Article  PubMed  Google Scholar 

  46. Marwaha JS, Chen HW, Habashy K, Choi J, Spain DA, Brat GA. Appraising the quality of development and reporting in surgical prediction models. JAMA Surg. 2023;158(2):214–6.

    Article  PubMed  Google Scholar 

  47. Liu X, Glocker B, McCradden MM, Ghassemi M, Denniston AK, Oakden-Rayner L. The medical algorithmic audit. Lancet Digital Health. 2022;4(5):e384–97.

    Article  CAS  PubMed  Google Scholar 

  48. Sendak MP, Gao M, Brajer N, Balu S. Presenting machine learning model information to clinical end users with model facts labels. NPJ Digital Med. 2020;3(1):41.

    Article  Google Scholar 

  49. Smits FJ, Henry AC, Besselink MG, Busch OR, van Eijck CH, Arntz M, Bollen TL, van Delden OM, van den Heuvel D, van der Leij C. Algorithm-based care versus usual care for the early recognition and management of complications after pancreatic resection in the Netherlands: an open-label, nationwide, stepped-wedge cluster-randomised trial. Lancet. 2022;399(10338):1867–75.

    Article  CAS  PubMed  Google Scholar 

  50. Wijnberge M, Geerts BF, Hol L, Lemmers N, Mulder MP, Berge P, Schenk J, Terwindt LE, Hollmann MW, Vlaar AP, et al. Effect of a machine learning-derived early warning system for intraoperative hypotension vs standard care on depth and duration of intraoperative hypotension during elective noncardiac surgery: the HYPE randomized clinical trial. JAMA. 2020;323(11):1052–60.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Ingraham NE, Jones EK, King S, Dries J, Phillips M, Loftus T, Evans HL, Melton GB, Tignanelli CJ. Re-aiming equity evaluation in clinical decision support: a scoping review of equity assessments in surgical decision support systems. Ann Surg. 2023;277(3):359–64.

    Article  PubMed  Google Scholar 

  52. Balch JA, Loftus TJ. Actionable artificial intelligence: overcoming barriers to adoption of prediction tools. Surgery. 2023.

  53. Lewis AE, Weiskopf N, Abrams ZB, Foraker R, Lai AM, Payne PR, Gupta A. Electronic health record data quality assessment and tools: a systematic review. J Am Med Inform Assoc. 2023;30(10):1730–40.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Beaulieu-Jones BK, Yuan W, Brat GA, Beam AL, Weber G, Ruffin M, Kohane IS. Machine learning for patient risk stratification: standing on, or looking over, the shoulders of clinicians? NPJ Digital Med. 2021;4(1):62.

  55. Finlayson SG, Subbaswamy A, Singh K, Bowers J, Kupke A, Zittrain J, Kohane IS, Saria S. The clinician and dataset shift in artificial intelligence. N Engl J Med. 2021;385(3):283–6.

    Article  PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

  1. Department of Surgery, University of Florida, Gainesville, FL, USA

    Jeremy A. Balch, Gilbert R. Upchurch Jr. & Tyler J. Loftus

  2. Department of Nephrology, University of Florida, Gainesville, FL, USA

    Benjamin Shickel & Azra Bihorac

  3. Division of Acute Care Surgery, Department of Surgery, University, of Florida College of Medicine, 600 SW Archer Road, PO Box 100119, Gainesville, FL, 32610-0119, USA

    Tyler J. Loftus

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  1. Jeremy A. Balch
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Correspondence to Tyler J. Loftus.

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Balch, J.A., Shickel, B., Bihorac, A. et al. Integration of AI in surgical decision support: improving clinical judgment. Global Surg Educ 3, 56 (2024). https://doi.org/10.1007/s44186-024-00257-2

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