Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress 2023 EHA Congress 2023 ASCO Annual Meeting
Clinical Collections
Advances in Alzheimer’s

At a glance: The ONWARDS insulin icodec trials

A round-up of the ONWARDS phase 3 clinical trials evaluating the novel weekly insulin icodec in people with diabetes.
ADVANCED SEARCH
Log in
Top
European Journal of Trauma and Emergency Surgery
Published in: European Journal of Trauma and Emergency Surgery 3/2021

01-06-2021 | Artificial Intelligence | Review Article

The automaton as a surgeon: the future of artificial intelligence in emergency and general surgery

Authors: Lara Rimmer, Callum Howard, Leonardo Picca, Mohamad Bashir

Published in: European Journal of Trauma and Emergency Surgery | Issue 3/2021

Login to get access

Abstract

Background

Artificial intelligence (AI) is a field involving computational simulation of human intelligence processes; these applications of deep learning could have implications in the specialty of emergency surgery (ES). ES is a rapidly advancing area, and this review will outline the most recent advances.

Methods

A literature search encompassing the uses of AI in surgery was conducted across large databases (Pubmed, OVID, SCOPUS). Two doctors (LR, CH) both collated relevant papers and appraised them. Papers included were published within the last 5 years, and a “snowball effect” used to collate further relevant literature.

Results

AI has been shown to provide value in predicting surgical outcomes and giving personalised patient risks based on inputted data. Further to this, image recognition technology within AI has showed success in fracture identification and breast cancer diagnosis. Regarding theatre presence, supervised robots have carried out suturing and anastomosis of bowel in controlled environments to a high standard.

Conclusion

AI has potential for integration across surgical services, from diagnosis to treatment, and aiding the surgeon in key decision-making for risks per patient. Fully automated surgery may be the future, but at present, AI needs human supervision.
Literature
1.
Bashir M, Harky A. Artificial intelligence in aortic surgery: the rise of the machine. Semin Thorac Cardiovasc Surg. 2019;31:635–7.PubMedCrossRef
2.
Maddox TM, Rumsfeld JS, Payne PRO. Questions for artificial intelligence in health care. JAMA J Am MedAssoc. 2019;321(1):31–2.CrossRef
3.
4.
5.
D. Silver et al., “A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play,” Science (80-.)., vol. 362, no. 6419, pp. 1140–1144, 2018.
6.
7.
Loftus T, et al. Artificial intelligence and Surgical decision-making. JAMA Surg. 2019;155(2):148–58.CrossRef
8.
Farahmand S, Shabestari O, Pakrah M, Hossein-Nejad H, Arbab M, Bagheri-Hariri S. Artificial intelligence-based triage for patients with acute abdominal pain in emergency department; a diagnostic accuracy study. Adv J Emerg Med. 2017;1(1):5.
9.
Zho S, Greenspan H, Shen D. Deep learning for medical image analysis. 2017.
10.
Esteva A, Kuprel B, Novoa R. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115–8.PubMedCrossRef
11.
Lakhani P, Sundaram B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology. 2017;284(2):574–82.PubMedCrossRef
12.
Ehteshami B, Veta M, Johannes van Diest P, van Ginneken B, Karssemeijer N, Litjens G. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA. 2017;318(22):2199–210.CrossRef
13.
K. Yasaka, H. Akai, O. Abe, and S. Kiryu, “Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced CT: a preliminary study.,” Radiology, 2017.
14.
Gulshan V, Peng L, Coram M. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402–10.PubMedCrossRef
15.
Reismann J, et al. Diagnosis and classification of pediatric acute appendicitis by artificial intelligence methods: an investigator-independent approach. PLoS ONE. 2019;14(9):1–11.CrossRef
16.
17.
Olczak J, et al. Artificial intelligence for analyzing orthopedic trauma radiographs: deep learning algorithms—are they on par with humans for diagnosing fractures? Acta Orthop. 2017;88(6):581–6.PubMedPubMedCentralCrossRef
18.
Cheng PM, Tejura TK, Tran KN, Whang G. Detection of high-grade small bowel obstruction on conventional radiography with convolutional neural networks. Abdom Radiol. 2018;43(5):1120–7.CrossRef
19.
Cheng PM, Tran KN, Whang G, Tejura TK. Refining convolutional neural network detection of small-bowel obstruction in conventional radiography. Am J Roentgenol. 2019;212(2):342–50.CrossRef
20.
Bilimoria KY, et al. Surgical risk calculator : a decision aide and informed consent tool for patients and surgeons. J Am Coll Surg. 2013;217(5):833–42.PubMedPubMedCentralCrossRef
21.
Bagnall NM, et al. Perioperative risk prediction in the era of enhanced recovery: a comparison of POSSUM, ACPGBI, and E-PASS scoring systems in major surgical procedures of the colorectal surgeon. Int J Colorectal Dis. 2018;33(11):1627–34.PubMedPubMedCentralCrossRef
22.
Shortliffe EH, Sepúlveda MJ. Clinical decision support in the era of artificial intelligence. J Am Med Assoc. 2018;320(21):2199–200.CrossRef
23.
24.
Hill B, et al. An automated machine learning-based model predicts postoperative mortality using readily-extractable preoperative electronic health record data. Br J Anaesth. 2019;123(6):877–86.PubMedPubMedCentralCrossRef
25.
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.PubMedCrossRef
26.
Corey KM, 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):1–19.CrossRef
27.
Bihorac 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.PubMedPubMedCentralCrossRef
28.
Hofer IS, Lee C, Gabel E, Baldi P, Cannesson M. Development and validation of a deep neural network model to predict postoperative mortality, acute kidney injury, and reintubation using a single feature set. Digit Med. 2020;3:1.
29.
Lei VJ, et al. Risk stratification for postoperative acute kidney injury in major noncardiac surgery using preoperative and intraoperative data. JAMA Netw Open. 2019;2(12):e1916921.PubMedPubMedCentralCrossRef
30.
Parreco J, Hidalgo A, Parks JJ, Kozol R, Rattan R. Using artificial intelligence to predict prolonged mechanical ventilation and tracheostomy placement. J Surg Res. 2018;228:179–87.PubMedCrossRef
31.
Kose E, Ozturk NN, Karahan SR. Artificial intelligence in surgery. Eur Arch Med Res. 2018;34(Suppl 1):4–6.CrossRef
32.
Panesar S, Cagle Y, Chander D, Morey J, Fernandez-Miranda J, Kliot M. Artificial intelligence and the future of surgical robotics. Ann Surg. 2019;270(2):223–6.PubMedCrossRef
33.
Aruni G, Amit G, Dasgupta P. New surgical robots on the horizon and the potential role of artificial intelligence. Investig Clin Urol. 2018;59(4):221–2.PubMedPubMedCentralCrossRef
34.
Saeidi H, Opfermann JD, Kam M, Raghunathan S, Leonard S, Krieger A. A confidence-based shared control strategy for the smart tissue autonomous robot (STAR). In: IEEE international conference intelligence robotics system. 2018. pp. 1268–1275.
35.
Kaan HL, Ho KY. Robot-assisted endoscopic resection: current status and future directions. Gut Liver. 2020;14(2):150–2.PubMedCrossRef
36.
Lin Y, Lin C. The application of artificial intelligence technology in the diagnosis of acute pancreatitis. In: Progn. Syst. Health Manag. Conf. 2019. pp. 244–8.
37.
Knoops PGM, et al. A machine learning framework for automated diagnosis and computer-assisted planning in plastic and reconstructive surgery. Sci Rep. 2019;9(1):1–12.CrossRef
38.
39.
Hung A, et al. Experts vs super-experts: differences in automated performance metrics and clinical outcomes for robot-assisted radical prostatectomy. BJU Int. 2018;123:5.
40.
Verghese A, Shah NH, Harrington RA. What this computer needs is a physician humanism and artificial intelligence. JAMA J Am Med Assoc. 2018;319(1):19–20.CrossRef
41.
O’Sullivan S, et al. Legal, regulatory, and ethical frameworks for development of standards in artificial intelligence (AI) and autonomous robotic surgery. Int J Med Robot Comput Assist Surg. 2019;15(1):1–12.
42.
Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019;17(1):1–9.CrossRef
43.
Badgeley MA, et al. Deep learning predicts hip fracture using confounding patient and healthcare variables. Digit Med. 2019;2:1.
44.
Pesapane F, Volonté C, Codari M, Sardanelli F. Artificial intelligence as a medical device in radiology: ethical and regulatory issues in Europe and the United States. Insights Imaging. 2018;9(5):745–53.PubMedPubMedCentralCrossRef
45.
King TC, Aggarwal N, Taddeo M, Floridi L. Artificial intelligence crime: an interdisciplinary analysis of foreseeable threats and solutions, vol. 26. Netherlands: Springer; 2020.
Metadata
Title
The automaton as a surgeon: the future of artificial intelligence in emergency and general surgery
Authors
Lara Rimmer
Callum Howard
Leonardo Picca
Mohamad Bashir
Publication date
01-06-2021
Publisher
Springer Berlin Heidelberg
Published in
European Journal of Trauma and Emergency Surgery / Issue 3/2021
Print ISSN: 1863-9933
Electronic ISSN: 1863-9941
DOI
https://doi.org/10.1007/s00068-020-01444-8

Other articles of this Issue 3/2021

European Journal of Trauma and Emergency Surgery 3/2021 Go to the issue

Original Article

The influence of the COVID-19 pandemic on traumatic brain injuries in Tyrol: experiences from a state under lockdown

Original Article

Predictors of 30-day mortality in orthogeriatric fracture patients aged 85 years or above admitted from the emergency department

Review Article

Mortality among hip fracture patients infected with COVID-19 perioperatively

Original Article

Nailing of proximal ulna fractures: biomechanical comparison of a new locked nail with angular stable plating

Editorial

Emergency surgery and trauma during COVID-19 pandemic: safe, smart and kind!

Original Article

Effects of COVID-19 pandemic on general surgical emergencies: are some emergencies really urgent? Level 1 trauma center experience