Artificial intelligence in oncology: global experience and future prospects

Objective: To analyze foreign experience in the use of artificial intelligence (AI) in oncology, as well as examine current advances in AI and its impact on clinical practice.

Material and methods. A systematic review of foreign literature was carried out, current AI programs and technologies in oncology were analyzed. The total number of identified records via the PubMed/MEDLINE search was 7680. After publication selection in accordance with the PRISMA guidelines, 32 studies that met all criteria were randomly included in the review and formed the basis for the analysis.

Results. AI demonstrates high efficiency in cancer diagnostics, including early detection of tumors, analysis of medical images and pathological data. A literature review on the use of AI models in oncology revealed exponential growth from 2010 to 2022, confirming the active development of the field. Diagnostic and treatment reports generated by the AI technologies indicated a comparable level of accuracy to that of experienced oncologists; they also revealed the ability to improve clinical outcomes. Furthermore, the introduction of AI stimulates the development of personalized treatment, increased patient adherence to therapy and optimization of the work of medical organizations. AI’s impact was revealed on physicians (reduced diagnostic and treatment errors), patients (personalized support), and hospitals (smart management systems).

Conclusion. AI is becoming an integral part of modern oncology, offering new opportunities to improve diagnostics, treatment, outcome prediction, and patient support. A study of the dynamics of publications and AI models indicates that the use of AI in clinical oncology is rapidly developing, opening up new prospects for the creation of smart hospitals, simplification of medical data exchange, development of personalized medicine, and improvement of the quality of patient care. The integration of AI with emerging technologies such as wearable devices and multimodal analysis promises to revolutionize oncology practice.

1. Kaul V., Enslin S., Gross S.A. History of artificial intelligence in medicine. Gastrointest Endosc. 2020; 92 (4): 807–12. https://doi.org/10.1016/j.gie.2020.06.040.

2. Hamamoto R., Suvarna K., Yamada M., et al. Application of artificial intelligence technology in oncology: towards the establishment of precision medicine. Cancers. 2020; 12 (12): 3532. https://doi.org/10.3390/cancers12123532.

3. Bhinder B., Gilvary C., Madhukar N.S., Elemento O. Artificial intelligence in cancer research and precision medicine. Cancer Discov. 2021; 11 (4): 900–15. https://doi.org/10.1158/2159-8290.CD-21-0090.

4. Lamotkin A.I., Korabelnikov D.I., Lamotkin I.A. Artificial intelligence: basic terms and concepts, the application in healthcare and clinical medicine. FARMAKOEKONOMIKA. Sovremennaya farmakoekonomika i farmakoepidemiologiya / FARMAKOEKONOMIKA. Modern Pharmacoeconomics and Pharmacoepidemiology. 2024; 17 (3): 409–15 (in Russ.). https://doi.org/10.17749/2070-4909/farmakoekonomika.2024.267.

5. Siegel R.L., Miller K.D., Fuchs H.E., Jemal A. Cancer statistics, 2021. CA Cancer J Clin. 2021; 71 (1): 7–33. https://doi.org/10.3322/caac.21654.

6. McKinney S.M., Sieniek M., Godbole V., et al. International evaluation of an AI system for breast cancer screening. Nature. 2020; 577 (7788): 89–94. https://doi.org/10.1038/s41586-019-1799-6.

7. Katzman J.L., Shaham U., Cloninger A., et al. DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Med Res Methodol. 2018; 18 (1): 24. https://doi.org/10.1186/s12874-018-0482-1.

8. Zadeh Shirazi A., Tofighi M., Gharavi A., Gomez G.A. The application of artificial intelligence to cancer research: a comprehensive guide. Technol Cancer Res Treat. 2024; 23: 15330338241250324. https://doi.org/10.1177/15330338241250324.

9. Itahashi K., Kondo S., Kubo T., et al. Evaluating clinical genome sequence analysis by Watson for Genomics. Front Med. 2018; 5: 305. https://doi.org/10.3389/fmed.2018.00305.

10. Guitton T., Allaume P., Rabilloud N., et al. Artificial intelligence in predicting microsatellite instability and KRAS, BRAF mutations from whole-slide images in colorectal cancer: a systematic review. Diagnostics. 2023; 14 (1): 99. https://doi.org/10.3390/diagnostics14010099.

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

12. Somashekhar S.P., Sepúlveda M.J., Puglielli S., et al. Watson for oncology and breast cancer treatment recommendations: agreement with an expert multidisciplinary tumor board. Ann Oncol. 2018; 29 (2): 418–23. https://doi.org/10.1093/annonc/mdx781.

13. McKinney S.M., Sieniek M., Godbole V., et al. International evaluation of an AI system for breast cancer screening. Nature. 2020; 577 (7788): 89–94. https://doi.org/10.1038/s41586-019-1799-6.

14. Dacic S., Travis W.D., Giltnane J.M., et al., Artificial intelligence (AI)- powered pathologic response (PathR) assessment of resection specimens after neoadjuvant atezolizumab in patients with non-small cell lung cancer: results from the LCMC3 study. J Clin Oncol. 2021; 39 (15): 106. https://doi.org/10.1200/JCO.2021.39.15_suppl.106.

15. Beaubier N., Tell R., Lau D., et al. Clinical validation of the tempus xT next-generation targeted oncology sequencing assay. Oncotarget. 2019; 10 (24): 2384–96. https://doi.org/10.18632/oncotarget.26797.

16. Coombs L., Orlando A., Wang X., et al. A machine learning framework supporting prospective clinical decisions applied to risk prediction in oncology. Digit Med. 2022; 5: 117. https://doi.org/10.1038/s41746-022-00660-3.

17. Bhattacharya S., Saleem S.M., Singh A., et al. Empowering precision medicine: regenerative AI in breast cancer. Front Oncol. 2024; 14: 1465720. https://doi.org/10.3389/fonc.2024.

18. Kim E.Y., Kim Y.J., Choi W.J., et al. Concordance rate of radiologists and a commercialized deep-learning solution for chest X-ray: real-world experience with a multicenter health screening cohort. PLoS One. 2022; 17 (2): e0264383. https://doi.org/10.1371/journal.pone.0264383.

19. Hussain S., Ali M., Naseem U., et al. Breast cancer risk prediction using machine learning: a systematic review. Front Oncol. 2024; 14: 1343627. https://doi.org/10.3389/fonc.2024.

20. Singh R.S., Masih G.D., Joshi R., et al. Chapter Five – Role of artificial intelligence in cancer diagnostics and therapeutics. In: Sobti R.C., Ganju A.K., Sobti A. (Eds) Biomarkers in cancer detection and monitoring of therapeutics. Academic Press; 2023: 83–97. https://doi.org/10.1016/B978-0-323-95116-6.00015-3.

21. Lamotkin A.I., Korabelnikov D.I., Lamotkin I.A., et al. Artificial intelligence in healthcare and medicine: the history of key events, its significance for doctors, the level of development in different countries. FARMAKOEKONOMIKA. Sovremennaya farmakoekonomika i farmakoepidemiologiya / FARMAKOEKONOMIKA. Modern Pharmacoeconomics and Pharmacoepidemiology. 2024; 17 (2): 243–50 (in Russ.). https://doi.org/10.17749/2070-4909/farmakoekonomika.2024.254.

22. Korabelnikov D.I., Lamotkin A.I. The effectiveness of using artificial intelligence in clinical medicine. FARMAKOEKONOMIKA. Sovremennaya farmakoekonomika i farmakoepidemiologiya / FARMAKOEKONOMIKA. Modern Pharmacoeconomics and Pharmacoepidemiology. 2025; 18 (1): 114–24 (in Russ.). https://doi.org/10.17749/2070-4909/ farmakoekonomika.2025.287.

23. Storås A.M., Strümke I., Riegler M.A., et al. Artificial intelligence in dry eye disease. Ocul Surf. 2022; 23: 74–86. https://doi.org/10.1016/j.jtos.2021.11.004.

24. Xu F., Wan C., Zhao L., et al. Predicting post-therapeutic visual acuity and OCT images in patients with central serous chorioretinopathy by artificial intelligence. Front Bioeng Biotechnol. 2021; 9: 649221. https://doi.org/10.3389/fbioe.2021.649221.

25. Tschandl P., Codella N., Akay B.N., et al. Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study. Lancet Oncol. 2019; 20 (7): 938–47. https://doi.org/10.1016/S1470-2045(19)30333-X.

26. Kann B.H., Hosny A., Aerts H. Artificial intelligence for clinical oncology. Cancer Cell. 2021; 39 (7): 916–27. https://doi.org/10.1016/j.ccell.2021.04.002.

27. De Silva D., Ranasinghe W., Bandaragoda T., et al. Machine learning to support social media empowered patients in cancer care and cancer treatment decisions. PLoS One. 2018; 13 (10): e0205855. https://doi.org/10.1371/journal.pone.0205855.

28. Abidi S.S. Knowledge management in healthcare: towards “knowledge-driven” decision-support services. Int J Med Inform. 2001; 63 (1–2): 5–18. https://doi.org/10.1016/s1386-5056(01)00167-8.

29. Hung C.Y., Chen H.Y., Wee L.J., et al. Deriving a novel health index using a large-scale population based electronic health record with deep networks. Annu Int Conf IEEE Eng Med Biol Soc. 2020; 2020: 5872–5. https://doi.org/10.1109/EMBC44109.2020.9176454.

30. Smrke U., Mlakar I., Lin S., et al. Language, speech, and facial expression features for artificial intelligence-based detection of cancer survivors' depression: scoping meta-review. JMIR Ment Health. 2021; 8 (12): e30439. https://doi.org/10.2196/30439.

31. Kang Y., Kim Y.J., Park S., et al. Development and operation of a digital platform for sharing pathology image data. BMC Med Inform Decis Mak. 2021; 21 (1): 114. https://doi.org/10.1186/s12911-021-01466-1.

32. Li J., Tian Y., Li R., et al. Improving prediction for medical institution with limited patient data: leveraging hospital-specific data based on multicenter collaborative research network. Artif Intell Med. 2021; 113: 102024. https://doi.org/10.1016/j.artmed.2021.102024.