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Artificial Intelligence in Perioperative Planning and Management of Liver Resection

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Abstract

Artificial intelligence (AI) is a speciality within computer science that deals with creating systems that can replicate the intelligence of a human mind and has problem-solving abilities. AI includes a diverse array of techniques and approaches such as machine learning, neural networks, natural language processing, robotics, and expert systems. An electronic literature search was conducted using the databases of “PubMed” and “Google Scholar”. The period for the search was from 2000 to June 2023. The search terms included “artificial intelligence”, “machine learning”, “liver cancers”, “liver tumors”, “hepatectomy”, “perioperative” and their synonyms in various combinations. The search also included all MeSH terms. The extracted articles were further reviewed in a step-wise manner for identification of relevant studies. A total of 148 articles were identified after the initial literature search. Initial review included screening of article titles for relevance and identifying duplicates. Finally, 65 articles were reviewed for this review article. The future of AI in liver cancer planning and management holds immense promise. AI-driven advancements will increasingly enable precise tumour detection, location, and characterisation through enhanced image analysis. ML algorithms will predict patient-specific treatment responses and complications, allowing for tailored therapies. Surgical robots and AI-guided procedures will enhance the precision of liver resections, reducing risks and improving outcomes. AI will also streamline patient monitoring, better hemodynamic management, enabling early detection of recurrence or complications. Moreover, AI will facilitate data-driven research, accelerating the development of novel treatments and therapies. Ultimately, AI’s integration will revolutionise liver cancer care, offering personalised, efficient and effective solutions, improving patients’ quality of life and survival rates.

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References

  1. Hamet P, Tremblay J (2017) Artificial intelligence in medicine. Metabolism 69S:S36–S40. https://doi.org/10.1016/j.metabol.2017.01.011

    Article  PubMed  CAS  Google Scholar 

  2. Perry CA (1990) Knowledge bases in medicine: a review. Bull Med Libr Assoc 78(3):271–282

    PubMed  PubMed Central  CAS  Google Scholar 

  3. Baxt WG (1995) Application of artificial neural networks to clinical medicine. Lancet 346(8983):1135–1138. https://doi.org/10.1016/s0140-6736(95)91804-3

    Article  PubMed  CAS  Google Scholar 

  4. Tarca AL, Carey VJ, Chen XW, Romero R, Drăghici S (2007) Machine learning and its applications to biology. PLoS Comput Biol 3(6):e116. https://doi.org/10.1371/journal.pcbi.0030116

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Vakalopoulou M, Christodoulidis S, Burgos N, Colliot O, Lepetit V (2023) Deep Learning: Basics and Convolutional Neural Networks (CNNs). In: Colliot O, editor. Machine Learning for Brain Disorders. New York, NY: Humana; 77–115. https://doi.org/10.1007/978-1-0716-3195-9_3

  6. Khan RA, Jawaid M, Khan AR, Sajjad M (2023) ChatGPT - Reshaping medical education and clinical management. Pak J Med Sci 39(2):605–607. https://doi.org/10.12669/pjms.39.2.7653

    Article  PubMed  PubMed Central  Google Scholar 

  7. Davenport T, Kalakota R (2019) The potential for artificial intelligence in healthcare. Future Healthc J 6(2):94–98. https://doi.org/10.7861/futurehosp.6-2-94

    Article  PubMed  PubMed Central  Google Scholar 

  8. Thrall JH, Li X, Li Q et al (2018) Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success. J Am Coll Radiol 15(3 Pt B):504–508. https://doi.org/10.1016/j.jacr.2017.12.026

    Article  PubMed  Google Scholar 

  9. Salto-Tellez M, Maxwell P, Hamilton P (2019) Artificial intelligence-the third revolution in pathology. Histopathology 74(3):372–376. https://doi.org/10.1111/his.13760

    Article  PubMed  Google Scholar 

  10. Hashimoto DA, Rosman G, Rus D, Meireles OR (2018) Artificial intelligence in surgery: promises and perils. Ann Surg 268(1):70–76. https://doi.org/10.1097/SLA.0000000000002693

    Article  PubMed  Google Scholar 

  11. Topol EJ (2019) High-performance medicine: the convergence of human and artificial intelligence. Nat Med 25(1):44–56. https://doi.org/10.1038/s41591-018-0300-7

    Article  PubMed  CAS  Google Scholar 

  12. Zhou N, Zhang CT, Lv HY et al (2019) Concordance study between IBM Watson for oncology and clinical practice for patients with cancer in China. Oncologist 24(6):812–819. https://doi.org/10.1634/theoncologist.2018-0255

    Article  PubMed  Google Scholar 

  13. Doyle-Lindrud S (2015) Watson will see you now: a supercomputer to help clinicians make informed treatment decisions. Clin J Oncol Nurs 19(1):31–32. https://doi.org/10.1188/15.CJON.31-32

    Article  PubMed  Google Scholar 

  14. Liu C, Liu X, Wu F, Xie M, Feng Y, Hu C (2018) Using artificial intelligence (Watson for Oncology) for treatment recommendations amongst Chinese patients with lung cancer: feasibility study. J Med Internet Res 20(9):e11087. https://doi.org/10.2196/11087. (Published 2018 Sep 25)

    Article  PubMed  PubMed Central  Google Scholar 

  15. Tian Y, Liu X, Wang Z et al (2020) Concordance between Watson for Oncology and a multidisciplinary clinical decision-making team for gastric cancer and the prognostic implications: retrospective study. J Med Internet Res. 22(2):e14122. https://doi.org/10.2196/14122. (Published 2020 Feb 20)

    Article  PubMed  PubMed Central  Google Scholar 

  16. Jumper J, Evans R, Pritzel A et al (2021) Highly accurate protein structure prediction with AlphaFold. Nature 596(7873):583–589. https://doi.org/10.1038/s41586-021-03819-2

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Yang T, Lin S, Xie Q et al (2019) Impact of 3D printing technology on the comprehension of surgical liver anatomy. Surg Endosc 33(2):411–417. https://doi.org/10.1007/s00464-018-6308-8

    Article  PubMed  Google Scholar 

  18. Mise Y, Hasegawa K, Satou S et al (2018) How has virtual hepatectomy changed the practice of liver surgery?: Experience of 1194 virtual hepatectomy before liver resection and living donor liver transplantation. Ann Surg 268(1):127–133. https://doi.org/10.1097/SLA.0000000000002213

    Article  PubMed  Google Scholar 

  19. Wang K, Mamidipalli A, Retson T, Bahrami N, Hasenstab K, Blansit K, Bass E, Delgado T, Cunha G, Middleton MS, Loomba R, Neuschwander-Tetri BA, Sirlin CB, Hsiao A, Members of the NASH Clinical Research Network (2019) Automated CT and MRI liver segmentation and biometry using a generalized convolutional neural network. Radiol Artif Intell 1:180022. https://doi.org/10.1148/ryai.2019180022

    Article  PubMed  PubMed Central  Google Scholar 

  20. Winkel DJ, Weikert TJ, Breit HC et al (2020) Validation of a fully automated liver segmentation algorithm using multi-scale deep reinforcement learning and comparison versus manual segmentation. Eur J Radiol 126:108918. https://doi.org/10.1016/j.ejrad.2020.108918

    Article  PubMed  Google Scholar 

  21. Veerankutty FH, Jayan G, Yadav MK et al (2021) Artificial Intelligence in hepatology, liver surgery and transplantation: emerging applications and frontiers of research. World J Hepatol 13(12):1977–1990. https://doi.org/10.4254/wjh.v13.i12.1977

    Article  PubMed  PubMed Central  Google Scholar 

  22. Durand F, Valla D (2005) Assessment of the prognosis of cirrhosis: Child-Pugh versus MELD. J Hepatol 42(1):S100–S107. https://doi.org/10.1016/j.jhep.2004.11.015

    Article  PubMed  Google Scholar 

  23. Johnson PJ, Berhane S, Kagebayashi C et al (2015) Assessment of liver function in patients with hepatocellular carcinoma: a new evidence-based approach-the ALBI grade. J Clin Oncol 33(6):550–558. https://doi.org/10.1200/JCO.2014.57.9151

    Article  PubMed  Google Scholar 

  24. Lu LH, Zhang YF, Mu-Yan C et al (2019) Platelet-albumin-bilirubin grade: risk stratification of liver failure, prognosis after resection for hepatocellular carcinoma. Dig Liver Dis 51(10):1430–1437. https://doi.org/10.1016/j.dld.2019.04.006

    Article  PubMed  CAS  Google Scholar 

  25. Zhou P, Chen B, Miao XY et al (2020) Comparison of FIB-4 index and Child-Pugh Score in predicting the outcome of hepatic resection for hepatocellular carcinoma. J Gastrointest Surg 24(4):823–831. https://doi.org/10.1007/s11605-019-04123-1

    Article  PubMed  Google Scholar 

  26. Mai RY, Lu HZ, Bai T et al (2020) Artificial neural network model for preoperative prediction of severe liver failure after hemihepatectomy in patients with hepatocellular carcinoma. Surgery 168(4):643–652. https://doi.org/10.1016/j.surg.2020.06.031

    Article  PubMed  Google Scholar 

  27. Ho WH, Lee KT, Chen HY, Ho TW, Chiu HC (2012) Disease-free survival after hepatic resection in hepatocellular carcinoma patients: a prediction approach using artificial neural network. PLoS ONE 7(1):e29179. https://doi.org/10.1371/journal.pone.0029179

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Saillard C, Schmauch B, Laifa O et al (2020) Predicting survival after hepatocellular carcinoma resection using deep learning on histological slides. Hepatology 72(6):2000–2013. https://doi.org/10.1002/hep.31207

    Article  PubMed  Google Scholar 

  29. Liu D, Liu F, Xie X et al (2020) Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound. Eur Radiol 30(4):2365–2376. https://doi.org/10.1007/s00330-019-06553-6

    Article  PubMed  Google Scholar 

  30. Hashimoto DA, Witkowski E, Gao L, Meireles O, Rosman G (2020) Artificial intelligence in anesthesiology: current techniques, clinical applications, and limitations. Anesthesiology 132(2):379–394. https://doi.org/10.1097/ALN.0000000000002960

    Article  PubMed  Google Scholar 

  31. Bilimoria KY, Liu Y, Paruch JL et al (2013) 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 217(5):833–42.e423. https://doi.org/10.1016/j.jamcollsurg.2013.07.385

    Article  PubMed  PubMed Central  Google Scholar 

  32. Bertsimas D, Dunn J, Velmahos GC, Kaafarani HMA (2018) 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 268(4):574–583. https://doi.org/10.1097/SLA.0000000000002956

    Article  PubMed  Google Scholar 

  33. El Hechi MW, Maurer LR, Levine J et al (2021) Validation of the artificial intelligence-based Predictive Optimal Trees in Emergency Surgery Risk (POTTER) calculator in emergency general surgery and emergency laparotomy paTients. J Am Coll Surg 232(6):912-919.e1. https://doi.org/10.1016/j.jamcollsurg.2021.02.009

    Article  PubMed  Google Scholar 

  34. Fleisher LA, Fleischmann KE, Auerbach AD et al (2014) 2014 ACC/AHA guideline on perioperative cardiovascular evaluation and management of patients undergoing noncardiac surgery: executive summary: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation 130(24):2215–2245. https://doi.org/10.1161/CIR.0000000000000105

    Article  PubMed  Google Scholar 

  35. Ford MK, Beattie WS, Wijeysundera DN (2010) Systematic review: prediction of perioperative cardiac complications and mortality by the revised cardiac risk index. Ann Intern Med 152(1):26–35. https://doi.org/10.7326/0003-4819-152-1-201001050-00007

    Article  PubMed  Google Scholar 

  36. Bihorac A, Ozrazgat-Baslanti T, Ebadi A et al (2019) MySurgeryRisk: development and validation of a machine-learning risk algorithm for major complications and death after surgery. Ann Surg 269(4):652–662. https://doi.org/10.1097/SLA.0000000000002706

    Article  PubMed  Google Scholar 

  37. Solanki SL, Kaur J, Gupta AM et al (2021) Cancer related nutritional and inflammatory markers as predictive parameters of immediate postoperative complications and long-term survival after hepatectomies. Surg Oncol 37:101526. https://doi.org/10.1016/j.suronc.2021.101526

    Article  PubMed  Google Scholar 

  38. Wang J, Zheng T, Liao Y et al (2022) Machine learning prediction model for post- hepatectomy liver failure in hepatocellular carcinoma: a multicenter study. Front Oncol 12:986867. https://doi.org/10.3389/fonc.2022.986867. (Published 2022 Nov 2)

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Connor CW, Segal S (2011) Accurate classification of difficult intubation by computerized facial analysis. Anesth Analg 112(1):84–93. https://doi.org/10.1213/ANE.0b013e31820098d6

    Article  PubMed  Google Scholar 

  40. Wang G, Li C, Tang F et al (2023) A fully-automatic semi-supervised deep learning model for difficult airway assessment. Heliyon 9(5):e15629. https://doi.org/10.1016/j.heliyon.2023.e15629

    Article  PubMed  PubMed Central  Google Scholar 

  41. Kendale S, Kulkarni P, Rosenberg AD, Wang J (2018) Supervised machine-learning predictive analytics for prediction of postinduction hypotension. Anesthesiology 129(4):675–688. https://doi.org/10.1097/ALN.0000000000002374

    Article  PubMed  Google Scholar 

  42. Hatib F, Jian Z, Buddi S et al (2018) Machine-learning algorithm to predict hypotension based on high-fidelity arterial pressure waveform analysis. Anesthesiology 129(4):663–674. https://doi.org/10.1097/ALN.0000000000002300

    Article  PubMed  Google Scholar 

  43. Gangakhedkar GR, Solanki SL, Divatia JV (2022) The use of Hypotension Prediction Index in cytoreductive surgery (CRS) with hyperthermic intraperitoneal chemotherapy (HIPEC). Indian J Anaesth 66(4):294–298. https://doi.org/10.4103/ija.ija_102_22

    Article  PubMed  PubMed Central  Google Scholar 

  44. Lee S, Lee HC, Chu YS et al (2021) Deep learning models for the prediction of intraoperative hypotension. Br J Anaesth 126(4):808–817. https://doi.org/10.1016/j.bja.2020.12.035

    Article  PubMed  Google Scholar 

  45. Veselis RA, Reinsel R, Sommer S, Carlon G (1991) Use of neural network analysis to classify electroencephalographic patterns against depth of midazolam sedation in intensive care unit patients. J Clin Monit 7(3):259–267. https://doi.org/10.1007/BF01619271

    Article  PubMed  CAS  Google Scholar 

  46. Ortolani O, Conti A, Di Filippo A et al (2002) EEG signal processing in anaesthesia. Use of a neural network technique for monitoring depth of anaesthesia. Br J Anaesth 88(5):644–648. https://doi.org/10.1093/bja/88.5.644

    Article  PubMed  CAS  Google Scholar 

  47. Mirsadeghi M, Behnam H, Shalbaf R, Jelveh MH (2016) Characterizing awake and anesthetized states using a dimensionality reduction method. J Med Syst 40(1):13. https://doi.org/10.1007/s10916-015-0382-4

    Article  PubMed  CAS  Google Scholar 

  48. Shalbaf A, Saffar M, Sleigh JW, Shalbaf R (2018) Monitoring the depth of anesthesia using a new adaptive neurofuzzy system. IEEE J Biomed Health Inform 22(3):671–677. https://doi.org/10.1109/JBHI.2017.2709841

    Article  PubMed  Google Scholar 

  49. Liu Q, Ma L, Fan SZ, Abbod MF, Shieh JS (2018) Sample entropy analysis for the estimating depth of anaesthesia through human EEG signal at different levels of unconsciousness during surgeries. PeerJ 6:e4817. https://doi.org/10.7717/peerj.4817. (Published 2018 May 23)

    Article  PubMed  PubMed Central  Google Scholar 

  50. Nagaraj SB, Biswal S, Boyle EJ et al (2017) Patient-specific classification of ICU sedation levels from heart rate variability. Crit Care Med 45(7):e683–e690. https://doi.org/10.1097/CCM.0000000000002364

    Article  PubMed  PubMed Central  Google Scholar 

  51. Solanki SL, Pandrowala S, Nayak A, Bhandare M, Ambulkar RP, Shrikhande SV (2021) Artificial intelligence in perioperative management of major gastrointestinal surgeries. World J Gastroenterol 27(21):2758–2770. https://doi.org/10.3748/wjg.v27.i21.2758

    Article  PubMed  PubMed Central  Google Scholar 

  52. Moore BLMBL, Panousis P, Kulkarni V, Pyeatt L, Doufas AGDAG (2010) Reinforcement learning for closed-loop propofol anesthesia: a human volunteer study. Proc AAAI Conf Artif Intell 24(2):1807–1813. https://doi.org/10.1609/aaai.v24i2.18817

    Article  Google Scholar 

  53. Yang N, Yang M, Peng W, et al (2020) Comparison of anesthesia effects between closed-loop and open-loop target controlled infusion of propofol using the bispectral index in abdominal surgery. Journal of Central South University. Med Sci 45(12):1419–1424. https://doi.org/10.11817/j.issn.1672-7347.2020.190489

  54. Puri GD, Mathew PJ, Biswas I et al (2016) A multicenter evaluation of a closed-loop anesthesia delivery system: a randomized controlled trial. Anesth Analg 122(1):106–114. https://doi.org/10.1213/ANE.0000000000000769

    Article  PubMed  CAS  Google Scholar 

  55. Avital G, Snider EJ, Berard D et al (2022) Closed-loop controlled fluid administration systems: a comprehensive scoping review. J Pers Med 12(7):1168. https://doi.org/10.3390/jpm12071168. (Published 2022 Jul 18)

    Article  PubMed  PubMed Central  Google Scholar 

  56. Chen L, Dubrawski A, Wang D et al (2016) Using supervised machine learning to classify real alerts and artifact in online multisignal vital sign monitoring data. Crit Care Med 44(7):e456–e463. https://doi.org/10.1097/CCM.0000000000001660

    Article  PubMed  PubMed Central  Google Scholar 

  57. Guillame-Bert M, Dubrawski A, Wang D, Hravnak M, Clermont G, Pinsky MR (2017) Learning temporal rules to forecast instability in continuously monitored patients. J Am Med Inform Assoc 24(1):47–53. https://doi.org/10.1093/jamia/ocw048

    Article  PubMed  Google Scholar 

  58. Rinehart J, Le Manach Y, Douiri H et al (2014) First closed-loop goal directed fluid therapy during surgery: a pilot study. Ann Fr Anesth Reanim 33(3):e35–e41. https://doi.org/10.1016/j.annfar.2013.11.016

    Article  PubMed  CAS  Google Scholar 

  59. Rinehart J, Lilot M, Lee C et al (2015) Closed-loop assisted versus manual goal-directed fluid therapy during high-risk abdominal surgery: a case-control study with propensity matching. Crit Care 19(1):94. https://doi.org/10.1186/s13054-015-0827-7. (Published 2015 Mar 19)

    Article  PubMed  PubMed Central  Google Scholar 

  60. Joosten A, Chirnoaga D, Van der Linden P et al (2021) Automated closed-loop versus manually controlled norepinephrine infusion in patients undergoing intermediate- to high-risk abdominal surgery: a randomised controlled trial. Br J Anaesth 126(1):210–218. https://doi.org/10.1016/j.bja.2020.08.051

    Article  PubMed  CAS  Google Scholar 

  61. Joosten A, Rinehart J, Van der Linden P et al (2021) Computer-assisted individualized hemodynamic management reduces intraoperative hypotension in intermediate- and high-risk surgery: a randomized controlled trial. Anesthesiology 135(2):258–272. https://doi.org/10.1097/ALN.0000000000003807

    Article  PubMed  CAS  Google Scholar 

  62. Pesteie M, Lessoway V, Abolmaesumi P, Rohling RN (2018) Automatic localization of the needle target for ultrasound-guided epidural injections. IEEE Trans Med Imaging 37(1):81–92. https://doi.org/10.1109/TMI.2017.2739110

    Article  PubMed  Google Scholar 

  63. Hetherington J, Lessoway V, Gunka V, Abolmaesumi P, Rohling R (2017) SLIDE: automatic spine level identification system using a deep convolutional neural network. Int J Comput Assist Radiol Surg 12(7):1189–1198. https://doi.org/10.1007/s11548-017-1575-8

    Article  PubMed  Google Scholar 

  64. Zhao Y, Zheng S, Cai N et al (2023) Utility of artificial intelligence for real-time anatomical landmark identification in ultrasound-guided thoracic paravertebral block. J Digit Imaging 36(5):2051–2059. https://doi.org/10.1007/s10278-023-00851-8

    Article  PubMed  Google Scholar 

  65. Bowness JS, Burckett-St Laurent D, Hernandez N et al (2023) Assistive artificial intelligence for ultrasound image interpretation in regional anaesthesia: an external validation study. Br J Anaesth 130(2):217–225. https://doi.org/10.1016/j.bja.2022.06.031

    Article  PubMed  Google Scholar 

  66. Alkhatib M, Hafiane A, Vieyres P, Delbos A (2019) Deep visual nerve tracking in ultrasound images. Comput Med Imaging Graph 76:101639. https://doi.org/10.1016/j.compmedimag.2019.05.007

    Article  PubMed  Google Scholar 

  67. Oh TT, Ikhsan M, Tan KK et al (2019) A novel approach to neuraxial anesthesia: application of an automated ultrasound spinal landmark identification. BMC Anesthesiol 19(1):57. https://doi.org/10.1186/s12871-019-0726-6. (Published 2019 Apr 16)

    Article  PubMed  PubMed Central  Google Scholar 

  68. Lucey P, Cohn JF, Matthews I et al (2011) Automatically detecting pain in video through facial action units. IEEE Trans Syst Man Cybern B Cybern 41(3):664–674. https://doi.org/10.1109/TSMCB.2010.2082525

    Article  PubMed  Google Scholar 

  69. Behrman M, Linder R, Assadi AH, Stacey BR, Backonja MM (2007) Classification of patients with pain based on neuropathic pain symptoms: comparison of an artificial neural network against an established scoring system. Eur J Pain 11(4):370–376. https://doi.org/10.1016/j.ejpain.2006.03.001

    Article  PubMed  Google Scholar 

  70. Lopez-Martinez D, Eschenfeldt P, Ostvar S, Ingram M, Hur C, Picard R (2019) Deep reinforcement learning for optimal critical care pain management with morphine using dueling double-deep Q networks. Annu Int Conf IEEE Eng Med Biol Soc 2019:3960–3963. https://doi.org/10.1109/EMBC.2019.8857295

    Article  PubMed  Google Scholar 

  71. Wang Q, Yuan L, Ding X, Zhou Z (2021) Prediction and diagnosis of venous thromboembolism using artificial intelligence approaches: a systematic review and meta-analysis. Clin Appl Thromb Hemost 27:10760296211021162. https://doi.org/10.1177/10760296211021162

    Article  PubMed  PubMed Central  Google Scholar 

  72. Ambra N, Mohammad OH, Naushad VA et al (2022) Venous thromboembolism among hospitalized patients: incidence and adequacy of thromboprophylaxis - a retrospective study. Vasc Health Risk Manag 18:575–587. https://doi.org/10.2147/VHRM.S370344. (Published 2022 Jul 24)

    Article  PubMed  PubMed Central  Google Scholar 

  73. Zhou S, Ma X, Jiang S, Huang X, You Y, Shang H, Lu Y (2021) A retrospective study on the effectiveness of Artificial Intelligence-based Clinical Decision Support System (AI-CDSS) to improve the incidence of hospital-related venous thromboembolism (VTE). Annals Of Translational Medicine 9(6):491. https://doi.org/10.21037/atm-21-1093

    Article  PubMed  PubMed Central  Google Scholar 

  74. Adams J (2023) Defending explicability as a principle for the ethics of artificial intelligence in medicine. Med Health Care Philos 26(4):615–623. https://doi.org/10.1007/s11019-023-10175-7

  75. Herington J, McCradden MD, Creel K et al (2023) Ethical considerations for artificial intelligence in medical imaging: deployment and governance. J Nucl Med 64(10):1509–1515. https://doi.org/10.2967/jnumed.123.266110

    Article  PubMed  Google Scholar 

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Authors and Affiliations

  1. Department of Anaesthesiology, Critical Care and Pain, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra, India

    Shruti Gairola & Sohan Lal Solanki

  2. Division of Hepatobiliary Surgical Oncology, Department of Surgical Oncology, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra, India

    Shraddha Patkar & Mahesh Goel

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Gairola, S., Solanki, S.L., Patkar, S. et al. Artificial Intelligence in Perioperative Planning and Management of Liver Resection. Indian J Surg Oncol (2024). https://doi.org/10.1007/s13193-024-01883-4

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