Artificial intelligence systems in surgery: A review of opportunities, limitations, and prospects
- Authors: Kobrinskii B.A.1
-
Affiliations:
- Federal Research Center «Computer Sciens and Control» Russian Academy of Sciences
- Issue: Vol 13, No 3 (2023)
- Pages: 385-404
- Section: Reviews
- URL: https://journals.rcsi.science/2219-4061/article/view/148337
- DOI: https://doi.org/10.17816/psaic1547
- ID: 148337
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Abstract
Artificial intelligence technologies are increasingly being applied in a variety of medical disciplines. After reviewing 278 publications from 1985 to 2023, 99 articles were selected from the databases elibrary, PubMed, Medline, WoS, Nature, Springer, and Wiley J Database to present the main approaches and a modern picture of the application of artificial intelligence methods and technologies in pediatric surgery and intensive care. The article examines many facets of artificial intelligence systems for medical uses, namely, computer decision support systems or supporting the surgeon throughout the surgical intervention procedure. Computer analysis of 3D visualization and 3D anatomical modeling of images obtained from computed tomography and magnetic resonance imaging investigations can be used to plan operations. Because of the possibilities of sufficiently accurate 3D models and methods for organs and pathological processes, various methodologies and software tools for preoperative planning and intraoperative support of surgical intervention have been developed. Computer (technical) vision analyzes high-quality medical images and interprets them in multimodal three-dimensional images for computer diagnoses and operations under visual control, including augmented reality methods. Robotic surgery involves manipulators, including remotely controlled ones, and intellectualized complexes that independently perform specific actions of the “second assistant surgeon”. In intensive care, artificial intelligence technologies are being investigated to merge data from bedside monitors and other information about patients’ conditions to identify critical situations and control mechanical ventilation. Simultaneously, several obstacles impede the adoption of artificial intelligence in surgery. The nature and standardization of the initial data required for their integration, taking into consideration atypical cases, the possibility of bias in the sample used, and the transparency of the decision-making process in machine learning models are examples. The explanation of solutions presented in machine learning models and the transition to full-fledged validation of the systems being built define the prospects for developing and using artificial intelligence systems.
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##article.viewOnOriginalSite##About the authors
Boris A. Kobrinskii
Federal Research Center «Computer Sciens and Control» Russian Academy of Sciences
Author for correspondence.
Email: kba_05@mail.ru
ORCID iD: 0000-0002-3459-8851
SPIN-code: 7075-7784
PhD, Dr. Sci. (Med.), Professor, Honored Scientist of the Russian Federation; Head of the Department of Intelligent Decision Support System; Chairman of the Scientific Council of the Russian Association of Artificial Intelligence
Russian Federation, MoscowReferences
- Gasparyan SA, Pashkina ES. Stranitsy istorii informatizatsii zdravookhraneniya Rossii. Moscow, 2002. 304 p. (In Russ.)
- Nazarenko GI, Osipov GS. Meditsinskie informatsionnye sistemy i iskusstvennyi intellekt. Moscow: Meditsina XXI, 2003. 234 p. (In Russ.)
- Zhai H, Brady P, Li Q, et al. Developing and evaluating a machine learning based algorithm to predict the need of pediatric intensive care unit transfer for newly hospitalized children. Resuscitation. 2014;85(8):1065–1071. doi: 10.1016/j.resuscitation.2014.04.009
- Rusin CG, Acosta SI, Shekerdemian LS, et al. Prediction of imminent, severe deterioration of children withparallel circulations using real-time processing of physiologic data. J Thorac Cardiovasc Surg. 2016;152(1):171–177. doi: 10.1016/j.jtcvs.2016.03.083
- Kwon J-M, Jeon K-H, Lee M, et al. Deep learning algorithm to predict need for critical care in pediatric emergency departments. Pediatr Emerg Care. 2021;37(12):e988–e994. doi: 10.1097/PEC.0000000000001858
- Park SJ, Cho K-J, Kwon O, et al. Development and validation of a deep-learning-based pediatric early warning system: a single-center study. Biomed J. 2022;45(1):155–168. doi: 10.1016/j.bj.2021.01.003
- Pospelov GS. Iskusstvennyi intellekt — osnova novoi informatsionnoi tekhnologii. Moscow: Nauka, 1988. 288 p. (In Russ.)
- Deo RC. Machine learning in medicine. Circulation. 2015;132(20):1920–1930. doi: 10.1161/CIRCULATIONAHA.115.001593
- Eckhardt CM, Madjarova SJ, Williams RJ, et al. Unsupervised machine learning methods and emerging applications in healthcare. Knee Surg Sports Traumatol Arthrosc. 2023;31:376–381. doi: 10.1007/s00167-022-07233-7
- Shah N, Arshad A, Mazer MB, et al. The use of machine learning and artificial intelligence within pediatric critical care. Pediatr Res. 2023;93(2):405–412. doi: 10.1038/s41390-022-02380-6
- Hopewell S, Loudon K, Clarke MJ, et al. Publication bias in clinical trials due to statistical significance or direction of trial results. Cochrane Database Syst Rev. 2009;1:MR000006. doi: 10.1002/14651858.MR000006.pub3
- Jüni P, Altman DG, Egger M. Systematic reviews in health care: assessing the quality of controlled clinical trials. BMJ. 2001;323(7303):42–46. doi: 10.1136/bmj.323.7303.42
- de Simone B, Chouillard E, Gumbs AA, et al. Artificial intelligence in surgery: the emergency surgeon’s perspective (the ARIES project). Discov Health Systems. 2022;1(1):9. doi: 10.1007/s44250-022-00014-6
- Joskowicz L. Computer-aided surgery meets predictive, preventive, and personalized medicine. EPMA J. 2017;8(1):1–4. doi: 10.1007/s13167-017-0084-8
- Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial intelligence in surgery: promises and perils. Ann Surg. 2018;268(1):70–76. doi: 10.1097/SLA.0000000000002693
- Dragun IA, Ustinov GG, Zatsepin PM. Avtomatizirovannaya sistema kolichestvennoi otsenki operatsionnogo riska. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering. 2007;310(1):217–221. (In Russ.)
- 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):574–583. doi: 10.1097/SLA.0000000000002956
- Maurer LR, Bertsimas D, Bouardi HT, et al. Trauma outcome predictor: an artificial intelligence interactive smartphone tool to predict outcomes in trauma patients. J Trauma Acute Care Surg. 2021;91(1):93–99. doi: 10.1097/TA.0000000000003158
- Thorsen-Meyer H-C, Nielsen AB, Nielsen AP, et al. Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records. Lancet Digit Health. 2020;2(4):e179–e191. doi: 10.1016/S2589-7500(20)30018-2
- Mountney P, Yang G-Z. Soft tissue tracking for minimally invasive surgery: learning local deformation online. Metaxas D, Axel L, Fichtinger G, Székely G, editors. Medical image computing and computer-assisted intervention – MICCAI 2008. MICCAI 2008. Lecture notes in computer science. Vol 5242. Springer, Berlin, Heidelberg. doi: 10.1007/978-3-540-85990-1_44.
- Vitiello V, Lee S-L, Cundy TP, Yang G-Z. Emerging robotic platforms for minimally invasive surgery. IEEE Rev Biomed Eng. 2013;6:111–126. doi: 10.1109/RBME.2012.2236311
- Chilamkurthy S, Ghosh R, Tanamala S, et al. Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. Lancet. 2018;392(10162):2388–2396. doi: 10.1016/S0140-6736(18)31645-3
- Daley M, Cameron S, Ganesan SL, et al. Pediatric severe traumatic brain injury mortality prediction determined with machine learning-based modeling. Injury. 2022;53(3):992–998. doi: 10.1016/j.injury.2022.01.008
- Kayhanian S, Young AMH, Mangla C, et al. Modelling outcomes after paediatric brain injury with admission laboratory values: a machine-learning approach. Pediatr Res. 2019;86(5):641–645. doi: 10.1038/s41390-019-0510-9
- Tunthanathip T, Oearsakul T. Application of machine learning to predict the outcome of pediatric traumatic brain injury. Chin J Traumatol. 2021;24(6):350–355. doi: 10.1016/j.cjtee.2021.06.003
- Meyer A, Zverinski D, Pfahringer B, et al. Machine learning for real-time prediction of complications in critical care: a retrospective study. Lancet Respir Med. 2018;6(12):905–914. doi: 10.1016/S2213-2600(18)30300-X
- Esteva H, Nunez TG, Rodriguez RO. Neural networks and artificial intelligence in thoracic surgery. Thorac Surg Clin. 2007;17(3):359–367. doi: 10.1016/j.thorsurg.2007.07.012
- Mayer-Shönberger V, Ingellson E. Big data and medicine: a big deal? J Intern Med. 2018;283(5):418–429. doi: 10.1111/joim.12721
- Wong A, Otles E, Donnelly JP, et al. External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Intern Med. 2021;181(8):1065–1070. doi: 10.1001/jamainternmed.2021.2626
- Hwang AB, Schuepfer G, Pietrini M, Boes S. External validation of EPIC’s risk of unplanned readmission model, the LACE+ index and SQLape as predictors of unplanned hospital readmissions: a monocentric, retrospective, diagnostic cohort study in Switzerland. PLoS One. 2021;16(11):e0258338. doi: 10.1371/journal.pone.0258338
- Shchadenko SV, Gorbachyova AS, Arslanova AR, Tolmachyov IV. 3D visualization for surgical modeling and surgical planning. Bulletin of Siberian Medicine. 2014;13(4):165–171. (In Russ.)
- Arhipov IV, Mikhaylov EM, Dolotova DD, et al. Evaluation of accuracy of medical optical navigation system “Neuroplan” for modeling of neurosurgical interventions. Russian journal of neurosurgery. 2018;20(4):104–113. (In Russ.) doi: 10.17650/1683-3295-2018-20-4-104-113
- Enchev Y. Neuronavigation: geneology, reality, and prospects. Neurosurg Focus. 2009;27(3):e11. doi: 10.3171/2009.6.FOCUS09109
- Monteiro M, Newcombe VFJ, Mathieu F, et al. Multiclass semantic segmentation and quantification of traumatic brain injury lesions on head CT using deep learning: an algorithm development and multicentre validation study. Lancet Digit Health. 2020;2(6):E314–E322. doi: 10.1016/S2589-7500(20)30085-6
- Dreizin D, Zhou Y, Fu S, et al. A multiscale deep learning method for quantitative visualization of traumatic hemoperitoneum at CT: assessment of feasibility and comparison with subjective categorical estimation. Radiol Artif Intell. 2020;2(6):e190220. doi: 10.1148/ryai.2020190220
- Wang X, Peng Y, Lu L, et al. ChestX-Ray8: Hospital-Scale chest X-Ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA. 2017. P. 3462–3471. doi: 10.1109/CVPR.2017.369
- Oakden-Rayner L. Exploring the ChestXray14 Dataset: Problems. Medical AI researcher. Radiologist Blog [cited 2023 Jun 21]. Available at: https://laurenoakdenrayner.com/2017/12/18/the-chestxray14-dataset-problems/
- Billinghurst M, Savage J, Oppenheimer P, Edmond C. The expert surgical assistant. An intelligent virtual environment with multimodal input. Stud Health Technol Inform. 1996;29:590–607.
- Savage J, Rosenblueth DA, Matamoros M, et al. Semantic reasoning in service robots using expert systems. Robotics Auton Syst. 2019;114:77–92. doi: 10.1016/j.robot.2019.01.007
- Mascagni P, Padoy N. OR black box and surgical control tower: recording and streaming data and analytics to improve surgical care. J Visc Surg. 2021;158(3S):18–25. doi: 10.1016/j.jviscsurg.2021.01.004
- Kenngott HG, Wagner M, Nickel F, et al. Computer-assisted abdominal surgery: new technologies. Langenbecks Arch Surg. 2015;400(3):273–281. doi: 10.1007/s00423-015-1289-8
- Mascagni P, Alapatt D, Urade T, et al. A computer vision platform to automatically locate critical events in surgical videos: documenting safety in laparoscopic cholecystectomy. Ann Surg. 2021;274(1):e93–e95. doi: 10.1097/SLA.0000000000004736
- Liu R, An J, Wang Z, et al. Artificial intelligence in laparoscopic cholecystectomy: does computer vision outperform human vision? Art Int Surg. 2022;2(2):80–92. doi: 10.20517/ais.2022.04
- Colleoni E, Moccia S, Du X, et al. Deep learning based robotic tool detection and articulation estimation with spatiotemporal layers. IEEE Robot Autom Lett. 2019;4(3):2714–2721. doi: 10.1109/LRA.2019.2917163
- Chadebecq F, Vasconcelos F, Mazomenos E, Stoyanov D. Computer vision in the surgical operating room. Visc Med. 2020;36(6):456–462. doi: 10.1159/000511934
- Ito K, Sugimoto M, Tsunoyama T, et al. A trauma patient care simulation using extended reality technology in the hybrid emergency room system. J Trauma Acute Care Surg. 2021;90(5):e108–e112. doi: 10.1097/TA.0000000000003086
- Luo H, Hu Q, Jia F. Details preserved unsupervised depth estimation by fusing traditional stereo knowledge from laparoscopic image. Healthcare Technol Lett. 2019;6(6):154–158. doi: 10.1049/htl.2019.0063
- Chen JH, Li Y, Gong JP, Yakun W. Application of da Vinci surgical robotic system in hepatobiliary surgery. Int J Surg Med. 2017;4(1):22–27. doi: 10.5455/ijsm.da-Vinci-surgical-robotic-system-in-hepatobiliary-surgery
- Nishikawa A, Hosoi T, Koara K, at el. Face MOUSe: A novel human-machine interface for controlling the position of a laparoscope. IEEE Transact Robotics Automat. 2003;19(5):825–841. doi: 10.1109/TRA.2003.81709
- Saeidi H, Opfermann JD, Kam M, et al. Autonomous robotic laparoscopic surgery for intestinal anastomosis. Sci Robot. 2022;7(62):eabj2908. doi: 10.1126/scirobotics.abj2908
- Kozlov YA, Mihan DZh, Novozhilov VA, Baradieva PZ. Robot-assisted surgery in children — state of the art and perspectives of the development. Russian Journal of Pediatric Surgery, Anesthesia and Intensive Care. 2015;5(3):63–68. (In Russ.) doi: 10.17816/psaic192
- Mosoyan MS, Fedorov DA, Osipov IB, et al. Robot-assisted bladder diverticulectomy in a 9-year-old boy. Russian Journal of Pediatric Surgery, Anesthesia and Intensive Care. 2023;13(1):53–61. (In Russ.) doi: 10.17816/psaic1305
- Pelizzo G, Nakib G, Romano P, et al. Five millimetre-instruments in paediatric robotic surgery: Advantages and shortcomings. Minima Invasiv Ther Allied Technol. 2014;24(3):1–6. doi: 10.3109/13645706.2014.975135
- Matson A, Sinha CK, Haddad M. Robotic pediatric surgery. Sinha CK, Davenport M, editors. Handbook of pediatric surgery. Springer, Cham, 2022. P. 565–575. doi: 10.1007/978-3-030-84467-7_68
- Van Mulken TJM, Schols RM, Scharmga AMJ, et al. First-in-human robotic supermicrosurgery using a dedicated microsurgical robot for treating breast cancer-related lymphedema: a randomized pilot trial. Nat Commun. 2020;11(1):757. doi: 10.1038/s41467-019-14188-w
- Gumbs AA, Alexander F, Karcz K, et al. White paper: definitions of artificial intelligence and autonomous actions in clinical surgery. Art Int Surg. 2022;2(2):93–100. doi: 10.20517/ais.2022.10
- Gumbs AA, Frigerio I, Spolverato G, et al. Artificial intelligence surgery: how do we get to autonomous actions in surgery? Sensors. 2021;21(16):5526. doi: 10.3390/s21165526
- Wesdorp NJ, Hellingman T, Jansma EP, et al. Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment. Eur J Nucl Med Mol Imaging. 2021;48:1785–1794. doi: 10.1007/s00259-020-05142-w
- Hanson CW III, Marshall BE. Artificial intelligence applications in the intensive care unit. Crit Care Med. 2001;29(2):427–435. doi: 10.1097/00003246-200102000-00038
- Kobrinskii BA, Taperova LN. Proekt meditsinskoi intellektual’noi sistemy real’nogo vremeni dlya reanimatsii. Yazykin IM, Rybina GV, Sinitsyn SV, editors. Sbornik nauchnykh trudov: “Nauchnaya sessiya MIFI-2007”. Vol. 3. Moscow, 2007. P. 32–34. (In Russ.)
- Kobrinskiy BA. Retrospective analysis of medical expert systems. Novosti iskusstvennogo intellekta. 2005;(2):6–17. (In Russ.)
- Gazizova DSh, Lishchuk VA, Lobacheva GV, et al. Primenenie matematicheskikh modelei i metodov dlya lecheniya ostroi serdechnoi nedostatochnosti. Lishchuk VA, Gazizova DSh, editors. Matematicheskaya kardiologiya. Teoriya, klinicheskie rezul’taty, rekomendatsii, perspektivy. Moscow: OOO «Print PRO», 2015. P. 145–146. (In Russ.)
- Shvyrev SL, Zarubina TV. Informatsionnye tekhnologii v intensivnoi terapii. Moscow: Izdatel’skii dom “Menedzher zdravookhraneniya”, 2016. 90 p. (In Russ.)
- Vincent J-L. The continuum of critical care. Crit Care. 2019;23(S1):122. doi: 10.1186/s13054-019-2393-x
- Chang H, Yu JY, Yoon S, Kim T, Cha WC. Machine learning-based suggestion for critical interventions in the management of potentially severe conditioned patients in emergency department triage. Sci Rep. 2022;12:10537. doi: 10.1038/s41598-022-14422-4
- Chen L, Ogundele O, Clermont G, et al. Dynamic and personalized risk forecast in step-down units. Implications for monitoring paradigms. Ann Am Thorac Soc. 2017;14(3):384–391. doi: 10.1513/AnnalsATS.201611-905OC
- Yoon JH, Mu L, Chen L, et al. Predicting tachycardia as a surrogate for instability in the intensive care unit. J Clin Monit Comput. 2019;33(6):973–985. doi: 10.1007/s10877-019-00277-0
- Wijnberge M, Geerts BF, Hol L, 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):1052–1060. doi: 10.1001/jama.2020.0592
- Yoon JH, Jeanselme V, Dubrawski A, et al. Prediction of hypotension events with physiologic vital sign signatures in the intensive care unit. Crit Care. 2020;24:661. doi: 10.1186/s13054-020-03379-3
- Gutierrez G. Artificial Intelligence in the Intensive Care Unit. Crit Care. 2020;24:101. doi: 10.1186/s13054-020-2785-y
- Banerjee I, Sofela M, Yang J, et al. Development and performance of the pulmonary embolism result forecast model (PERFORM) for computed tomography clinical decision support. JAMA Netw Open. 2019;2(8):e198719. doi: 10.1001/jamanetworkopen.2019.8719
- Zeiberg D, Prahlad T, Nallamothu BK, et al. Machine learning for patient risk stratification for acute respiratory distress syndrome. PLoS One. 2019;14(3):e0214465. doi: 10.1371/journal.pone.0214465
- Flechet M, Falini S, Bonetti C, et al. Machine learning versus physicians’ prediction of acute kidney injury in critically ill adults: a prospective evaluation of the AKIpredictor. Crit Care. 2019;23:282. doi: 10.1186/s13054-019-2563-x
- Tran NK, Sen S, Palmieri TL, et al. Artificial intelligence and machine learning for predicting acute kidney injury in severely burned patients: a proof of concept. Burns. 2019;45(6):1350–1358. doi: 10.1016/j.burns.2019.03.021
- Davoudi A, Malhotra KR, Shickel B, et al. Intelligent ICU for autonomous patient monitoring using pervasive sensing and deep learning. Sci Rep. 2019;9(1):8020. doi: 10.1038/s41598-019-44004-w
- Ma P, Liu J, Shen F, et al. Individualized resuscitation strategy for septic shock formalized by finite mixture modeling and dynamic treatment regimen. Crit Care. 2021;25:243. doi: 10.1186/s13054-021-03682-7
- Demšar J, Zupan B. Hands-on training about overfitting. PLoS Comput Biol. 2021;17:1008671. doi: 10.1371/journal.pcbi.1008671
- Hravnak M, Pellathy T, Chen L, et al. A call to alarms: current state and future directions in the battle against alarm fatigue. J Electrocardiol. 2018;51(6S):44–48. doi: 10.1016/j.jelectrocard.2018.07.024
- Morris AH. Human cognitive limitations. Broad, consistent, clinical application of physiological principles will require decision support. Ann Am Thorac Soc. 2018;15(S1):53–56. doi: 10.1513/AnnalsATS.201706-449KV
- Parreco J, Hidalgo A, Parks JJ, et al. Using artificial intelligence to predict prolonged mechanical ventilation and tracheostomy placement. J Surg Res. 2018;228:179–187. doi: 10.1016/j.jss.2018.03.028
- Hsieh M-H, Hsieh M-J, Chen C-M, et al. An artificial neural network model for predicting successful extubation in intensive care units. J Clin Med. 2018;7(9):240. doi: 10.3390/jcm7090240
- Thille AW, Rodriguez P, Cabello B, et al. Patient-ventilator asynchrony during assisted mechanical ventilation. Intensive Care Med. 2006;32(10):1515–1522. doi: 10.1007/s00134-006-0301-8
- Chen C-W, Lin W-C, Hsu C-H, et al. Detecting ineffective triggering in the expiratory phase in mechanically ventilated patients based on airway flow and pressure deflection: feasibility of using a computer algorithm. Crit Care Med. 2008;36(2):455–461. doi: 10.1097/01.CCM.0000299734.34469.D9
- Blanch L, Sales B, Montanya J, et al. Validation of the better care system to detect ineffective efforts during expiration in mechanically ventilated patients: a pilot study. Intensive Care Med. 2012;38(5):772–780. doi: 10.1007/s00134-012-2493-4
- Marchuk Y, Magrans R, Sales B, et al. Predicting patient-ventilator asynchronies with hidden Markov models. Sci Rep. 2018;8:17614. doi: 10.1038/s41598-018-36011-0
- Yoon JH, Pinsky MR, Clermont G. Artificial intelligence in critical care medicine. Crit Care. 2022;26(1):75. doi: 10.1186/s13054-022-03915-3
- Natarajan P. Best practices: Separating myth from reality. Natarajan P, Frenzel JC, Smaltz DH, editors. Demystifying big data and machine learning for healthcare. 1st edition. CRC Press, 2023. P. 56–68. doi: 10.1201/9781315389325
- Petersen E, Holm S, Ganz M, Feragen A. The path toward equal performance in medical machine learning. Patterns. 2023;4(7):100790. doi: 10.1016/j.patter.2023.100790
- Litvin A, Korenev S, Rumovskaya S, et al. WSES project on decision support systems based on artificial neural networks in emergency surgery. World J Emerg Surg. 2021;16(1):50. doi: 10.1186/s13017-021-00394-9
- Steyerberg EW, Vergouwe Y. Towards better clinical prediction models: Seven steps for development and an ABCD for validation. Eur Heart J. 2014;35(29):1925–1931. doi: 10.1093/eurheartj/ehu207
- Leisman DE, Harhay MO, Lederer DJ, et al. Development and reporting of prediction models: guidance for authors from editors of Respiratory, Sleep, and Critical Care journals. Crit Care Med. 2020;48(5):623–633. doi: 10.1097/CCM.0000000000004246
- Duran JM, Jongsma KR. Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI. J Med Ethics. 2021;47:329–335. doi: 10.1136/medethics-2020-106820
- Linardatos P, Papastefanopoulos V, Kotsiantis S. Explainable AI: a review of machine learning interpretability methods. Entropy. 2020;23(1):18. doi: 10.3390/e23010018
- Finn VK, Shesternikova OP. Ehvristika obnaruzheniya ehmpiricheskikh zakonomernostei posredstvom DSM-rassuzhdenii. Nauchno-tekhnicheskaya informatsiya. Seriya 2: Informatsionnye protsessy i sistemy. 2018;(9):7–42. (In Russ.)
- Gavrilov AV. Gibridnye intellektual’nye sistemy: Monografiya. Novosibirsk: Izd-vo NGTU, 2002. (In Russ.)
- Ignatyev VV. Adaptive hybrid intellectual control systems. Izvestiya SFEDU. Engineering sciences. 2010;(12):89–94. (In Russ.)
- Kobrinskii BA, Dolotova DD, Donitova VV, Gavrilov AV. Radiological images in the construction of hybrid intelligent system. Medical doctor and IT. 2020;(4):43–50. (In Russ.) doi: 10.37690/1811-0193-2020-4-43-50
- Kitaguchi D, Takeshita N, Hasegawa H, Ito M. Artificial intelligence-based computer vision in surgery: Recent advances and future perspectives. Ann Gastroenterol Surg. 2022;6(1):29–36. doi: 10.1002/ags3.12513
- Camarillo DB, Krummel TM, Salisbury JK. Robotic technology in surgery: past, present, and future. Am J Surg. 2018;188(4S1):2–15. doi: 10.1016/j.amjsurg.2004.08.025