Новые возможности искусственного интеллекта в медицине: описательный обзор

   Целью описательного обзора литературы является рассмотрение новых возможностей искусственного интеллекта (ИИ) для улучшения диагностики, профилактики и лечения различных заболеваний.

   В статье обсуждается, какие современные инструменты ИИ могут использоваться в клинической практике, организации здравоохранения и медицинском образовании. В работе рассмотрены различные аспекты систем ИИ медицинского назначения, которые в основном являются системами компьютерной поддержки принятия врачебных решений в процессе клинической работы. Большое внимание уделено возможностям генеративного ИИ в медицине. Были исследованы потенциальные возможности применения ИИ в клинической практике, что подчеркнуло многообещающие перспективы как для практикующих врачей, так и для их пациентов. Описываются ограничения, связанные с использованием ИИ в различных областях медицины, предлагаются возможные способы их решения. Излагаются проблемы информационной безопасности и этических ограничений, связанные с внедрением ИИ. Широкая интеграция ИИ в сферу общественного здравоохранения повысит качество поддержки принятия клинических и управленческих решений, ускорит диагностику заболеваний и улучшит общее качество и доступность медицинских услуг.

1. Russell SJ, Norvig P. Artificial intelligence a modern approach. London, 2010, 1132 p.

2. Sheikhtaheri A, Sadoughi F, Hashemi Dehaghi Z. Developing and using expert systems and neural networks in medicine: a review on benefits and challenges. J Med Syst. 2014;38(9):110 doi: 10.1007/s10916-014-0110-5

3. Michalski RS, Carbonell JG, Mitchell TM. (ed.). Machine learning: An artificial intelligence approach. Springer Science & Business Media, 2013. doi: 10.1007/978-3-662-12405-5

4. Deo RC. Machine Learning in Medicine. Circulation. 2015;132(20):1920-1930. doi: 10.1161/CIRCULATIONAHA.115.001593

5. Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer. 2018;18(8):500-510. doi: 10.1038/s41568-018-0016-5

6. Zakhem GA, Motosko CC, Ho RS. How Should Artificial Intelligence Screen for Skin Cancer and Deliver Diagnostic Predictions to Patients? JAMA Dermatol. 2018;154(12):1383-1384. doi: 10.1001/jamadermatol.2018.2714

7. Badrulhisham F, Pogatzki-Zahn E, Segelcke D, Spisak T, Vollert J. Machine learning and artificial intelligence in neuroscience: A primer for researchers. Brain Behav Immun. 2024;115:470-479. doi: 10.1016/j.bbi.2023.11.005

8. Loftus TJ, Tighe PJ, Filiberto AC, Efron PA, Brakenridge SC, Mohr AM, et al. Artificial Intelligence and Surgical Decision-making. JAMA Surg. 2020;155(2):148-158. doi: 10.1001/jamasurg.2019.4917

9. Salto-Tellez M, Maxwell P, Hamilton P. Artificial intelligence-the third revolution in pathology. Histopathology. 2019;74(3):372-376. doi: 10.1111/his.13760

10. Zhang P, Kamel Boulos MN. Generative AI in Medicine and Healthcare: Promises, Opportunities and Challenges. Future Internet. 2023; 15(9):286. doi: 10.3390/fi15090286

11. Dale R. GPT-3: What’s it good for? Natural Language Engineering. 2021;27(1):113-118. doi: 10.1017/S1351324920000601

12. Aydın Ö, Karaarslan E. OpenAI ChatGPT Generated Literature Review: Digital Twin in Healthcare. 2022:22-31. doi: 10.2139/ssrn.4308687

13. Doan S, Conway M, Phuong TM, Ohno-Machado L. Natural language processing in biomedicine: a unified system architecture overview. Methods Mol Biol. 2014;1168:275-294. doi: 10.1007/978-1-4939-0847-9_16

14. Hossain E, Rana R, Higgins N, Soar J, Barua PD, Pisani AR, Turner K. Natural Language Processing in Electronic Health Records in relation to healthcare decision-making : A systematic review. Comput Biol Med. 2023;155:106649. doi: 10.1016/j.compbiomed.2023.106649

15. Morris MX, Song EY, Rajesh A, Kass N, Asaad M, Phillips BT. New Frontiers of Natural Language Processing in Surgery. Am Surg. 2023;89(1):43-48. doi: 10.1177/00031348221117039

16. Meskó B, Topol EJ. The imperative for regulatory oversight of large language models (or generative AI) in healthcare. NPJ Digit Med. 2023;6(1):120. doi: 10.1038/s41746-023-00873-0

17. Thirunavukarasu AJ, Ting DSJ, Elangovan K, Gutierrez L, Tan TF, Ting DSW. Large language models in medicine. Nat Med. 2023;29(8):1930-1940. doi: 10.1038/s41591-023-02448-8

18. Bobba PS, Sailer A, Pruneski JA, Beck S, Mozayan A, Mozayan S, et al. Natural language processing in radiology: Clinical applications and future directions. Clin Imaging. 2023;97:55-61. doi: 10.1016/j.clinimag.2023.02.014

19. Marinov Z, Jaeger PF, Egger J, Kleesiek J, Stiefelhagen R. Deep Interactive Segmentation of Medical Images : A Systematic Review and Taxonomy. arXiv. 2023;2311.13964. doi: 10.48550/arXiv.2311.13964

20. Alowais SA, Alghamdi SS, Alsuhebany N, Alqahtani T, Alshaya AI, Almohareb SN, et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med Educ. 2023;23,689. doi: 10.1186/s12909-023-04698-z

21. Thirunavukarasu AJ, Elangovan K, Gutierrez L, Li Y, Tan I, Keane PA, et al. Democratizing Artificial Intelligence Imaging Analysis with Automated Machine Learning: Tutorial. Journal of Medical Internet Research. 2023;25. doi: 10.2196/49949

22. Schlam I, Saad Menezes MC, Corti C, Tan A, Abuali I, Tolaney SM. Artificial intelligence as an adjunct tool for breast oncologists – are we there yet? ESMO Open. 2023;8(5). doi: 10.1016/j.esmoop.2023.101643

23. Vorontsov E, Bozkurt A, Casson A, Shaikovski G, Zelechowski M, Liu S, et al. Virchow: A Million-Slide Digital Pathology Foundation Model. arXiv, 28 Oct 2023. doi: 10.48550/arXiv.2309.07778

24. Muhiyaddin R, Abd-Alrazaq AA, Househ M, Alam T, Shah Z. The Impact of Clinical Decision Support Systems (CDSS) on Physicians: A Scoping Review. Stud Health Technol Inform. 2020;272:470-473. doi: 10.3233/SHTI200597

25. Hassan N, Slight R, Morgan G, Bates DW, Gallier S, Sapey E, Slight S. Road map for clinicians to develop and evaluate AI predictive models to inform clinical decision-making. BMJ Health Care Inform.2023;30:e100784. doi: 10.1136/bmjhci-2023-100784

26. Jiao W, Zhang X, D’souza F. The Economic Value and Clinical Impact of Artificial Intelligence in Healthcare : A Scoping Literature Review. IEEE Access. 2023;11:123445-123457. doi: 10.1109/ACCESS.2023.3327905

27. Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023;25:e49324. doi: 10.2196/49324

28. Thapa S, Adhikari S. ChatGPT, Bard, and Large Language Models for Biomedical Research: Opportunities and Pitfalls. Ann Biomed Eng. 2023;51(12):2647-2651. doi: 10.1007/s10439-023-03284-0

29. Mohsen F, Ali H, El Hajj N, Shah Z. Artificial intelligence-based methods for fusion of electronic health records and imaging data. Sci Rep. 2022;12(1):17981. doi: 10.1038/s41598-022-22514-4

30. Srivastava J, Routray S, Ahmad S, Waris MM. Internet of Medical Things (IoMT)-Based Smart Healthcare System: Trends and Progress. Comput Intell Neurosci. 2022;2022:7218113. doi: 10.1155/2022/7218113

31. Uysal MP. Machine learning-enabled healthcare information systems in view of Industrial Information Integration Engineering. Journal of Industrial Information Integration. 2022;30:100382. doi: 10.1016/j.jii.2022.100382

32. Rumovskaya S, Litvin A. Capabilities of Distributed Artificial Intelligence in Medicine, 5^th International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA), Lipetsk, Russian Federation, 2023;424-429. doi: 10.1109/SUMMA60232.2023.10349394

33. Curtis RG, Bartel B, Ferguson T, Blake HT, Northcott C, Virgara R, Maher CA. Improving User Experience of Virtual Health Assistants: Scoping Review. J Med Internet Res. 2021;23(12):e31737. doi: 10.2196/31737

34. Sun G, Zhou YH. AI in healthcare: navigating opportunities and challenges in digital communication. Front Digit Health. 2023;5:1291132. doi: 10.3389/fdgth.2023.1291132

35. Nourse R, Dingler T, Kelly J, Kwasnicka D, Maddison R. The Role of a Smart Health Ecosystem in Transforming the Management of Chronic Health Conditions. J Med Internet Res. 2023;25:e44265. doi: 10.2196/44265

36. Eriksen AV, Møller S, Ryg J. Use of GPT-4 to Diagnose Complex Clinical Cases. NEJM AI. 2023;1(1). doi: 10.1056/AIp2300031

37. Blanco-González A, Cabezón A, Seco-González A, Conde-Torres D, Antelo-Riveiro P, Piñeiro Á, Garcia-Fandino R. The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies. Pharmaceuticals. 2023;16(6):891. doi: 10.3390/ph16060891

38. Qureshi R, Irfan M, Gondal TM, Khan S, Wu J, Hadi MU, et al. AI in drug discovery and its clinical relevance. Heliyon. 2023;9(7):e17575. doi: 10.1016/j.heliyon.2023.e17575

39. Chalasani SH, Syed J, Ramesh M, Patil V, Pramod Kumar TM. Artificial intelligence in the field of pharmacy practice : A literature review. Explor Res Clin Soc Pharm. 2023;12:100346. doi: 10.1016/j.rcsop.2023.100346

40. Giuffrè M, Shung DL. Harnessing the power of synthetic data in healthcare: innovation, application, and privacy. NPJ Digit Med. 2023;6(1):186. doi: 10.1038/s41746-023-00927-3

41. Muniz-Terrera G, Mendelevitch O, Barnes R, Lesh MD. Virtual Cohorts and Synthetic Data in Dementia: An Illustration of Their Potential to Advance Research. Front Artif Intell. 2021;4:613956. doi: 10.3389/frai.2021.613956

42. Sabzalieva E, Valentini A. ChatGPT and Artificial Intelligence in Higher Education: Quick Start Guide. UNESCO. 2023. [Electronic resource]. [date of access 2023 December 8]. Available from: https://www.iesalc.unesco.org/wp-content/uploads/2023/04/ChatGPT-and-Artificial-Intelligence-in-higher-education-Quick-Start-guide_EN_FINAL.pdf

43. Ghorashi N, Ismail A, Ghosh P, Sidawy A, Javan R. AI-Powered Chatbots in Medical Education: Potential Applications and Implications. Cureus. 2023;15(8):e43271. doi: 10.7759/cureus.43271

44. Nagi F, Salih R, Alzubaidi M, Shah H, Alam T, Shah Z, Househ M. Applications of Artificial Intelligence (AI) in Medical Education: A Scoping Review. Stud Health Technol Inform. 2023;305:648-651. doi: 10.3233/SHTI230581

45. Peshkova M, Yumasheva V, Rudenko E, Kretova N, Timashev P, Demura T. Digital twin concept: Healthcare, education, research. J Pathol Inform. 2023;14:100313. doi: 10.1016/j.jpi.2023.100313

46. Webster P. Six ways large language models are changing healthcare. Nat Med. 2023;29(12):2969-2971. doi: 10.1038/s41591-023-02700-1

47. Rajpurkar P, Chen E, Banerjee O, Topol EJ. AI in health and medicine. Nat Med. 2022;28(1):31-38. doi: 10.1038/s41591-021-01614-0

48. Kelly BS, Judge C, Bollard SM, Clifford SM, Healy GM, Aziz A, et al. Radiology artificial intelligence : a systematic review and evaluation of methods (RAISE). Eur Radiol. 2022;32(11):7998-8007. doi: 10.1007/s00330-022-08784-6

49. Chan HCS, Shan H, Dahoun T, Vogel H, Yuan S. Advancing Drug Discovery via Artificial Intelligence. Trends Pharmacol Sci. 2019;40(8):592-604. doi: 10.1016/j.tips.2019.06.004

50. Sun T, Niu X, He Q, Chen F, Qi RQ. Artificial Intelligence in microbiomes analysis : A review of applications in dermatology. Front Microbiol. 2023;14:1112010. doi: 10.3389/fmicb.2023.1112010

51. Sheikh A, Anderson M, Albala S, Casadei B, Franklin BD, Richards M, et al. Health information technology and digital innovation for national learning health and care systems. Lancet Digit Health. 2021;3(6):e383-e396. doi: 10.1016/S2589-7500(21)00005-4

52. Noorbakhsh-Sabet N, Zand R, Zhang Y, Abedi V. Artificial Intelligence Transforms the Future of Health Care. Am J Med. 2019;132(7):795-801. doi: 10.1016/j.amjmed.2019.01.017

53. Hunter DJ, Holmes C. Where Medical Statistics Meets Artificial Intelligence. N Engl J Med. 2023;389(13):1211-1219. doi: 10.1056/NEJMra2212850

54. Ellis RJ, Sander RM, Limon A. Twelve key challenges in medical machine learning and solutions. Intelligence-Based Medicine. 2022;6:100068. doi: 10.1016/j.ibmed.2022.100068

55. World Health Organization. (‎2023)‎. Regulatory considerations on artificial intelligence for health. ISBN: 9789240078871 [Electronic resource]. [date of access 2024 January 12]. Available from: https://www.who.int/publications/i/item/9789240078871

56. Vo V, Chen G, Aquino YSJ, Carter SM, Do QN, Woode ME. Multi-stakeholder preferences for the use of artificial intelligence in healthcare : A systematic review and thematic analysis. Soc Sci Med. 2023;338:116357. doi: 10.1016/j.socscimed.2023.116357

57. Elendu C, Amaechi DC, Elendu TC, Jingwa KA, Okoye OK, John Okah M, et al. Ethical implications of AI and robotics in healthcare : A review. Medicine (Baltimore).2023;102(50):e36671. doi: 10.1097/MD.0000000000036671

58. Russell RG, Lovett Novak L, Patel M, Garvey Kim V, Kelly JT, Gretchen JP, et al. Competencies for the Use of Artificial Intelligence-Based Tools by Health Care Professionals. Acad Med. 2023;98(3):348-356. doi: 10.1097/ACM.0000000000004963

59. Wubineh BZ, Deriba FG, Woldeyohannis MM. Exploring the opportunities and challenges of implementing artificial intelligence in healthcare : A systematic literature review. Urol Oncol. 2023. doi: 10.1016/j.urolonc.2023.11.019

60. McGowan A, Gui Y, Dobbs M, Shuster S, Cotter M, Selloni A, et al. ChatGPT and Bard exhibit spontaneous citation fabrication during psychiatry literature search. Psychiatry Res. 2023;326:115334. doi: 10.1016/j.psychres.2023.115334

61. Seghier ML. ChatGPT: not all languages are equal. Nature. 2023;615(7951):216. doi: 10.1038/d41586-023-00680-3

62. Yang YC, Islam SU, Noor A, Khan S, Afsar W, Nazir S. Influential Usage of Big Data and Artificial Intelligence in Health-care. Comput Math Methods Med. 2021;2021:5812499. doi: 10.1155/2021/5812499

63. Межведомственный исследовательский центр искусственного интеллекта [Электронный ресурс]. [дата обращения 27 января 2024]. Режим доступа: http://uiip.bas-net.by/intellekt/

64. Абламейко С. Искусственный интеллект в Беларуси – история и перспективы. Наука и инновации.2022;(5):26-31. [дата обращения 27 января 2024]. Режим доступа: https://innosfera.belnauka.by/jour/article/download/62/61

65. Платформа Искусственного Интеллекта [Электронный ресурс]. [дата обращения 27 января 2024]. Режим доступа: https://belai.by/

66. Wang F, Preininger A. AI in Health: State of the Art, Challenges, and Future Directions. Yearb Med Inform.2019;28(1):16-26. doi: 10.1055/s-0039-1677908

67. Gartner Top 10 Strategic Technology Trends for 2024 [Electronic resource]. [date of access 2024 January 12]. Available from: https://www.gartner.com/en/articles/gartner-top-10-strategic-technology-trends-for-2024

68. Sabry Abdel-Messih M, Kamel Boulos MN. ChatGPT in Clinical Toxicology. JMIR Med Educ. 2023;9:e46876. doi: 10.2196/46876