Page 85 - Read Online

McGivern et al. Art Int Surg 2023;3:27-47 https://dx.doi.org/10.20517/ais.2022.39 Page 43

Availability of data and materials
Not applicable.

Financial support and sponsorship
None.

Conflicts of interest
All authors declared that there are no conflicts of interest.

Ethical approval and consent to participate
Not applicable.

Consent for publication
Not applicable.

Copyright
© The Author(s) 2023.

REFERENCES
1. McCarthy J. What is artificial intelligence? Available from: https://www.diochnos.com/about/McCarthyWhatisAI.pdf [Last accessed
on 23 Mar 2023].
2. Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial intelligence in surgery: promises and perils. Available from: https://
journals.lww.com/annalsofsurgery/Abstract/2018/07000/Artificial_Intelligence_in_Surgery__Promises_and.13.aspx [Last accessed
on 23 Mar 2023].
3. Gumbs AA, Alexander F, Karcz K, et al. White paper: definitions of artificial intelligence and autonomous actions in clinical surgery.
Art Int Surg 2022;2:93-100. DOI
4. Gumbs AA, Perretta S, d’Allemagne B, Chouillard E. What is Artificial Intelligence Surgery? Art Int Surg 2021;1:1-10. DOI
5. Elyan E, Vuttipittayamongkol P, Johnston P, et al. Computer vision and machine learning for medical image analysis: recent
advances, challenges, and way forward. Art Int Surg ;2022:2. DOI
6. Bari H, Wadhwani S, Dasari BVM. Role of artificial intelligence in hepatobiliary and pancreatic surgery. World J Gastrointest Surg
2021;13:7-18. DOI PubMed PMC
7. Mauro A, Greco M, Grimaldi M. What is big data? Am J Phys 2015;1644:97-104. DOI
8. Vedula SS, Hager GD. Surgical data science: The new knowledge domain. Innov Surg Sci 2017;2:109-21. DOI PubMed PMC
9. NHS England. 2022/23 priorities and operational planning guidance. Available from: https://www.england.nhs.uk/wp-content/
uploads/2022/02/20211223-B1160-2022-23-priorities-and-operational-planning-guidance-v3.2.pdf [Last accessed on 23 Mar 2023].
10. Tricco AC, Lillie E, Zarin W, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern
Med 2018;169:467-73. DOI PubMed
11. Knight SR, Ots R, Maimbo M, Drake TM, Fairfield CJ, Harrison EM. Systematic review of the use of big data to improve surgery in
low- and middle-income countries. Br J Surg 2019;106:e62-72. DOI PubMed PMC
12. Covidence. Veritas health innovation, Melbourne, Australia. Available from: https://www.covidence.org/ [Last accessed on 23 Mar
2023].
13. Săftoiu A, Vilmann P, Gorunescu F, et al. Efficacy of an artificial neural network-based approach to endoscopic ultrasound
elastography in diagnosis of focal pancreatic masses. Clin Gastroenterol Hepatol 2012;10:84-90.e1. DOI PubMed
14. Wu K, Chen X, Ding M. Deep learning based classification of focal liver lesions with contrast-enhanced ultrasound. Optik
2014;125:4057-63. DOI
15. Gatos I, Tsantis S, Karamesini M, Skouroliakou A, Kagadis G. Development of a support vector machine - based image analysis
system for focal liver lesions classification in magnetic resonance images. J Phys Conf Ser 2015;633:012116. DOI
16. Roch AM, Mehrabi S, Krishnan A, et al. Automated pancreatic cyst screening using natural language processing: a new tool in the
early detection of pancreatic cancer. HPB 2015;17:447-53. DOI PubMed PMC
17. Sada Y, Hou J, Richardson P, El-Serag H, Davila J. Validation of case finding algorithms for hepatocellular cancer from
administrative data and electronic health records using natural language processing. Med Care 2016;54:e9-14. DOI PubMed PMC
18. Kondo S, Takagi K, Nishida M, et al. Computer-aided diagnosis of focal liver lesions using contrast-enhanced ultrasonography with
perflubutane microbubbles. IEEE Trans Med Imaging 2017;36:1427-37. DOI PubMed
19. Yang H, Zhang X, Cai XY, et al. From big data to diagnosis and prognosis: gene expression signatures in liver hepatocellular
carcinoma. PeerJ 2017;5:e3089. DOI PubMed PMC