Understanding Artificial Intelligence and Applications in Oncology: Narrative Review
Received Date : 18 Jul 2023
Accepted Date : 31 Oct 2023
Available Online : 15 Nov 2023
Doi: 10.37047/jos.2023-98875 - Article's Language: EN
Journal of Oncological Sciences. 2024;10(1):39-46.
This is an open access article under the CC BY-NC-ND license
There is a rapid progression of information technologies in healthcare. One of the cornerstones of the process is AI, which uses a combination of multiple technologies in modern healthcare applications and has both administrative and clinical aspects. AI assists in the field of medicine by modifying the existing disease prevention procedures, diagnosis, treatment, drug development, clinical care, and other healthcare services; moreover, it is a promising modality in almost every field. In contrast, ethical issues such as accountability, personal data protection, patient safety, application bias, and trust issues are still debatable as they have not been fully addressed by AI. These discussions are based on the inadequacy of existing procedures, standards, and ethical rules for the ever-evolving AI technologies. Additionally, AI should be most intensively used for patients with oncologyand oncological applications. Thus, this article aimed to provide deep insights for a simple understanding of AI for oncologists.
  1. Kurt R. Industry 4.0 in terms of industrial relations and its impacts on labour life. Procedia Computer Science. 2019 January;158:590-601. [Crossref] 
  2. Federspiel F, Mitchell R, Asokan A, Umana C, McCoy D. Threats by artificial intelligence to human health and human existence. BMJ Glob Health. 2023;8(5):e010435. [Crossref]  [PubMed]  [PMC] 
  3. World Health Organization. Ethics and Governance of Artificial Intelligence for Health. Geneva: World Health Organization; 2021 Cited: August 3, 2023. Available from: [Link] 
  4. The History of Artificial Intelligence. Cited: August 3, 2023. Available from: [Link] 
  5. Taye MM. Understanding of machine learning with deep learning: architectures, workflow, applications and future directions. Computers. 2023;12(5):91. [Crossref] 
  6. Vorobiev I, Samsonovich AV. A conceptually different approach to the empirical test of alan turing. Procedia Computer Science. 2018 January;123:512-521. [Crossref] 
  7. Haenlein M. Kaplan A. A brief history of artificial intelligence: on the past, present, and future of artificial intelligence. Calif Manag. Rev. 2019;61(4):5-14. [Crossref] 
  8. Şeyh H, Prins C, Schrijvers E. Artificial intelligence: definition and background. In: Mission AI, ed. Research for Policy. Cham: Springer; 2023. p.15-41. [Crossref] 
  9. Krittanawong C, Zhang H, Wang Z, Aydar M, Kitai T. Artificial intelligence in precision cardiovascular medicine. J Am Coll Cardiol. 2017;69(21):2657-2664. [Crossref]  [PubMed] 
  10. Choi RY, Coyner AS, Kalpathy-Cramer J, Chiang MF, Campbell JP. Introduction to machine learning, neural networks, and deep learning. Transl Vis Sci Technol. 2020;9(2):14. [PubMed]  [PMC] 
  11. Aboagye EO. Gee CJ. Kumar R. Evaluating the performance of deep neural networks for health decision making. Procedia Computer Science. 2018 January;131:866-872. [Crossref] 
  12. Zhang Q, Yu H, Barbiero M, Wang B, Gu M. Artificial neural networks enabled by nanophotonics. Light Sci Appl. 2019 May 8;8:42. [Crossref]  [PubMed]  [PMC] 
  13. Borges AF, Laurindo FJ, Spinola MM, Gonçalves RF, Mattos CA. The strategic use of artificial intelligence in the digital era: systematic literature review and future research directions. International Journal of Information Management. 2021 April;57:102225. [Crossref] 
  14. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. May 2015;521:43644. [Crossref]  [PubMed] 
  15. Janiesch C, Zschech P, Heinrich K. Machine learning and deep learning. Electron Markets. 2021 April;31:685-695. [Crossref] 
  16. Geeks for Geeks [Internet]. [Cited: August 3, 2023]. Activation Functions. Available from: [Link] 
  17. Geeks for Geeks [Internet]. [Cited: August 3, 2023]. Underfitting and Overfitting. Available from: [Link] 
  18. Russell SJ. Norvig P. Artificial Intelligence: A Modern Approach. 4th ed. Harlow, United Kingdom: Pearson; 2021.
  19. Jordan MI, Mitchell TM. Machine learning: trends, perspectives, and prospects. Science. 2015;349(6245):255-260. [Crossref]  [PubMed] 
  20. Farina E, Nabhen JJ, Dacoregio MI, Batalini F, Moraes FY. An overview of artificial intelligence in oncology. Future Sci OA. 2022;8(4):FSO787. [Crossref]  [PubMed]  [PMC] 
  21. Wang Z, Liu Y, Niu X. Application of artificial intelligence for improving early detection and prediction of therapeutic outcomes for gastric cancer in the era of precision oncology. Semin Cancer Biol. 2023 Aug;93:83-96. [Crossref]  [PubMed] 
  22. Uzun T. [Artificial intelligence and health practices]. İzmir Katip Çelebi Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi. 2020;3(1):80-92. [Link] 
  23. STM ThinkTech. İleri Sağlık Teknolojileri I-Akıllı Sağlık Uygulamaları ve Veri Analizi İle Sağlık Sorunlarını Tanımlamak. 2019. Erişim tarihi: 03 Ağustos 2023. Erişim linki: [Link] 
  24. Markoff J. Microsoft Finds Cancer Clues in Search Queries. New York Times. June 7, 2016. [Link] 
  25. Pathway OME Announces Strategic Partnership with Standard Process and Rainbow Genomics.2018. [Link] 
  26. Akalın B, Veranyurt U. [Artificial intelligence in health services and management]. Acta Infologica. 2021;5(1):231-240. [Crossref] 
  27. Lee D. Yoon SN. Application of artificial intelligence-based technologies in the healthcare industry: opportunities and challenges. Int. J Environ Res Public Health. 2021;18(1):271. [Crossref]  [PubMed]  [PMC] 
  28. Kumar P. Chauhan S. Awasthi LK. Artificial intelligence in healthcare: review, ethics, trust challenges & future research directions. Engineering Applications of Artificial Intelligence. 2023 April:120. [Crossref] 
  29. Elkhader J, Elemento O. Artificial intelligence in oncology: from bench to clinic. Seminars in Cancer Biology. 2022 September;84:113-128. [Crossref]  [PubMed]