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Clinical trial digitalization: new opportunities for the use of artificial intelligence

https://doi.org/10.37489/2588-0519-2025-3-62-72

EDN: FAVBZS

Abstract

Background. The introduction of artificial intelligence (AI) technologies in clinical trials (CTs) opens up new horizons for drug development, but it is associated with significant methodological and regulatory challenges. The gap between the speed of technological progress and its practical implementation necessitates the development of comprehensive approaches for the effective integration of AI into research practice.

Objective. To summarize and systematize the key areas of AI application at all stages of the clinical trial life cycle, identify existing barriers, and propose a comprehensive model to overcome them.

Materials and methods. A systematic analysis and generalization of data from current scientific publications, regulatory documents, and methodological recommendations on the use of AI in clinical trials was conducted (during 01.09.2019 по 28.08.2025 yy). The concept of a multilevel AI architecture, including perceptual, cognitive, and decision-making intelligence, was used as a basis for structuring the material.

Results. In the course of the analysis, the key areas of AI application were identified and characterized in detail: from the development of a study design and optimization of patient recruitment using digital twins to decentralized data monitoring and predictive analysis of adverse events. The main barriers that hinder the widespread adoption of AI have been identified: data quality and representativeness problems, model insufficient interpretability, lack of unified validation standards, and legal uncertainty. A multilevel model for AI integration is proposed, covering the technological, organizational, ethical, and regulatory aspects.

Conclusion. The full integration of AI into clinical trials can dramatically increase their effectiveness and reduce the time and cost of developing new drugs. We believe that overcoming the existing barriers requires coordinated efforts of the scientific community, regulatory authorities, and the pharmaceutical industry to create a single ecosystem that ensures the transparency, reliability, and ethics of the use of digital technologies.

About the Authors

S. K. Zyryanov
Peoples’ Friendship University of Russia (RUDN University)
Russian Federation

Sergey K. Zyryanov — Dr. Sci. (Med.), Professor, Department of General and Clinical Pharmacology

Moscow


Competing Interests:

The authors declare no conflict of interest



M. A. Parshenkov
First Moscow State Medical University (Sechenov University)
Russian Federation

Mikhail A. Parshenkov — Junior Researcher at the Department of Toxicological and Pharmaceutical Chemistry named after A. P. Arzamastsev, A. P. Nelyubin Institute of Pharmacy

Moscow


Competing Interests:

The authors declare no conflict of interest



A. N. Yavorskiy
Association of Participants in the Circulation of Medicines and Medical Devices "LEKMEDOBRACHENIE"
Russian Federation

Alexander N. Yavorsky — Dr. Sci. (Med.), Professor, Advisor to the Director General

Moscow


Competing Interests:

The authors declare no conflict of interest



References

1. Pharmaceutical industry 4.0. Digital transformation / edited by A.L. Khokhlov, N.V. Pyatigorskaya. — Moscow: OKI Publishing House, 2025. — 312 p.: color ill. 37. ISBN 978-5-4465-4468-4. Режим доступа: https://izdat-oki.ru/farmacevticheskaya-otrasl-4-0-cifrovaya-transformaciya.

2. Karpov O.E., Khramov A.E. Information technology, computing systems and artificial intelligence in medicine. – M.: DPK Press; 2022. – 480 s. (In Russ.). ISBN 978-5-91976-232-4.

3. Poroikov VV. Komp'iuternoe konstruirovanie lekarstv: ot poiska novykh farmakologicheskikh veshchestv do sistemnoĭ farmakologii [Computer-aided drug design: from discovery of novel pharmaceutical agents to systems pharmacology]. Biomed Khim. 2020 Jan;66(1):30-41. doi: 10.18097/PBMC20206601030. (In Russ.).

4. Zhang K, Meng X, Yan X, et al. Revolutionizing Health Care: The Transformative Impact of Large Language Models in Medicine. J Med Internet Res. 2025 Jan 7;27:e59069. doi: 10.2196/59069.

5. Malheiro V, Santos B, Figueiras A, Mascarenhas-Melo F. The Potential of Artificial Intelligence in Pharmaceutical Innovation: From Drug Discovery to Clinical Trials. Pharmaceuticals (Basel). 2025 May 25;18(6):788. doi: 10.3390/ph18060788.

6. Decree of the President of the Russian Federation No. 490 of October 10, 2019 (as amended on February 15, 2024) "On the development of artificial intelligence in the Russian Federation.". (In Russ.). Доступно по: https://www.consultant.ru. Дата обращения: 02.08.2025.

7. European Parliament. EU AI Act: first regulation on artificial intelligence. – 2024. European Parliament. [European Parliament. EU AI Act: first regulation on artificial intelligence. European Parliament; 2024. Available from: https://www.europarl.europa.eu/topics/en/article/20230601STO93804/eu-ai-act-first-regulation-on-artificial-intelligence. Accessed 2025 Aug 08].

8. The White House. Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. – 2023. [The White House. Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. 2023. Available from: https://www.whitehouse.gov/briefing-room/presidential-actions/executive-order-on-the-safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence/. Accessed 2025 Aug 08.].

9. Office of the National Coordinator for Health Information Technology, Department of Health and Human Services. Health Data, Technology, and Interoperability: Certification Program Updates, Algorithm Transparency, and Information Sharing. 45 CFR §170, 171. – 2024. [Office of the National Coordinator for Health Information Technology, Department of Health and Human Services. Health Data, Technology, and Interoperability: Certification Program Updates, Algorithm Transparency, and Information Sharing. 45 CFR §170, 171. 2024. Available from: https://www.healthit.gov/topic/interoperability/health-data-technology-and-interoperability-certification-program-updates-algorithm-transparency-and-information-sharing. Accessed 2025 Aug 08.].

10. Koshechkin KA, Lebedev GS, Fartushnyi EN, Orlov YL. Holistic Approach for Artificial Intelligence Implementation in Pharmaceutical Products Lifecycle: A Meta-Analysis. Applied Sciences. 2022; 12(16):8373. https://doi.org/10.3390/app12168373.

11. Svechkareva IR, Gusev AV, Kolbin AS. Artificial intelligence in preclinical studies and clinical trials. Klini cheskaya farmakologiya i terapiya = Clin Pharmacol Ther 2025;34(1):14-19 (In Russ.)]. DOI 10.32756/0869-5490-2025 1-14-19.

12. Ahmed MI, Spooner B, Isherwood J, et al. A Systematic Review of the Barriers to the Implementation of Artificial Intelligence in Healthcare. Cureus. 2023 Oct 4;15(10):e46454. doi: 10.7759/cureus.46454.

13. Lu SC, Swisher CL, Chung C, et al. On the importance of interpretable machine learning predictions to inform clinical decision making in oncology. Front Oncol. 2023 Feb 28;13:1129380. doi: 10.3389/fonc.2023.1129380.

14. Chin MH, Afsar-Manesh N, Bierman AS, et al. Guiding Principles to Address the Impact of Algorithm Bias on Racial and Ethnic Disparities in Health and Health Care. JAMA Netw Open. 2023 Dec 1;6(12):e2345050. doi: 10.1001/jamanetworkopen.2023.45050.

15. Ueda D, Kakinuma T, Fujita S, et al. Fairness of artificial intelligence in healthcare: review and recommendations. Jpn J Radiol. 2024 Jan;42(1): 3-15. doi: 10.1007/s11604-023-01474-3.

16. Warraich HJ, Tazbaz T, Califf RM. FDA Perspective on the Regulation of Artificial Intelligence in Health Care and Biomedicine. JAMA. 2025 Jan 21;333(3):241-247. doi: 10.1001/jama.2024.21451.

17. Pavuluri S, Sangal R, Sather J, Taylor RA. Balancing act: the complex role of artificial intelligence in addressing burnout and healthcare workforce dynamics. BMJ Health Care Inform. 2024 Aug 24;31(1):e101120. doi: 10.1136/bmjhci-2024-101120.

18. Mumtaz H, Riaz MH, Wajid H, et al. Current challenges and potential solutions to the use of digital health technologies in evidence generation: a narrative review. Front Digit Health. 2023 Sep 28;5:1203945. doi: 10.3389/fdgth.2023.1203945.

19. Esmaeilzadeh P. Challenges and strategies for wide-scale artificial intelligence (AI) deployment in healthcare practices: A perspective for healthcare organizations. Artif Intell Med. 2024 May;151:102861. doi: 10.1016/j.artmed.2024.102861.

20. Clinical project management. / Belousov D.YU., Zyryanov S.K., Kolbin A.S. – 1-e izd. – M.: Buki Vedi: Izdatel’stvo OKI; 2017. – 676 s. (In Russ.). ISBN 978-5-4465-1602-5. Режим доступа: https://izdat-oki.ru/upravlenie_klinicheskimi_issledovaniymi.

21. You JG, Hernandez-Boussard T, Pfeffer MA, et al. Clinical trials informed framework for real world clinical implementation and deployment of artificial intelligence applications. NPJ Digit Med. 2025 Feb 17;8(1):107. doi: 10.1038/s41746-025-01506-4.

22. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019 Jan;25(1):44-56. doi: 10.1038/s41591-018-0300-7.

23. Akinrinmade AO, Adebile TM, Ezuma-Ebong C, et al. Artificial Intelligence in Healthcare: Perception and Reality. Cureus. 2023 Sep 20;15(9):e45594. doi: 10.7759/cureus.45594.

24. Costello J, Kaur M, Reformat MZ, Bolduc FV. Leveraging Knowledge Graphs and Natural Language Processing for Automated Web Resource Labeling and Knowledge Mobilization in Neurodevelopmental Disorders: Development and Usability Study. J Med Internet Res. 2023 Apr 17;25:e45268. doi: 10.2196/45268.

25. Chen Z, Liang N, Zhang H, et al. Harnessing the power of clinical decision support systems: challenges and opportunities. Open Heart. 2023 Nov 28;10(2):e002432. doi: 10.1136/openhrt-2023-002432.

26. Reason T, Langham J, Gimblett A. Automated Mass Extraction of Over 680,000 PICOs from Clinical Study Abstracts Using Generative AI: A Proof-of-Concept Study. Pharmaceut Med. 2024 Sep;38(5):365-372. doi: 10.1007/s40290-024-00539-6.

27. Cheng AC, Banasiewicz MK, Johnson JD, et al. Evaluating automated electronic case report form data entry from electronic health records. J Clin Transl Sci. 2022 Dec 14;7(1):e29. doi: 10.1017/cts.2022.514.

28. Harrer S, Shah P, Antony B, Hu J. Artificial Intelligence for Clinical Trial Design. Trends Pharmacol Sci. 2019 Aug;40(8):577-591. doi: 10.1016/j.tips.2019.05.005.

29. Kudrin R, Bushmakin I, Novitskaia O, et al. Multimodal AI engine for clinical trials outcome prediction: prospective case study H2 2023. 2023. doi: 10.13140/RG.2.2.10165.24809.

30. Denniston AK, Liu X. Responsible and evidence-based AI: 5 years on. Lancet Digit Health. 2024 May;6(5):e305-e307. doi: 10.1016/S2589-7500(24)00071-2.

31. Agboola OE, Agboola SS, Odeghe OB, et al. Computational Genome Engineering Through AI-CRISPR-Precision Medicine Integration in Modern Therapeutics. Ann Pharm Fr. 2025 Aug 7:S0003- 4509(25)00117-8. doi: 10.1016/j.pharma.2025.08.001.

32. Norori N, Hu Q, Aellen FM, et al. Addressing bias in big data and AI for health care: A call for open science. Patterns (N Y). 2021 Oct 8;2(10):100347. doi: 10.1016/j.patter.2021.100347.

33. Saint James Aquino Y. Making decisions: Bias in artificial intelligence and data driven diagnostic tools. Aust J Gen Pract. 2023 Jul;52(7):439- 442. doi: 10.31128/AJGP-12-22-6630.

34. Gross CP, Mallory R, Heiat A, Krumholz HM. Reporting the recruitment process in clinical trials: who are these patients and how did they get there? Ann Intern Med. 2002 Jul 2;137(1):10-6. doi: 10.7326/0003-4819-137-1-200207020-00007.

35. Nashwan AJ, Hani SB. Transforming cancer clinical trials: The integral role of artificial intelligence in electronic health records for efficient patient recruitment. Contemp Clin Trials Commun. 2023 Nov 7;36:101223. doi: 10.1016/j.conctc.2023.101223.

36. Miyasato G, Kasivajjala VC, Misra M, et al. AI-driven real-time patient identification for randomized controlled trials. J Clin Oncol. 2023;41(16_ suppl):e13565. doi:10.1200/JCO.2023.41.16_suppl.e13565.

37. Ismail A, Al-Zoubi T, El Naqa I, Saeed H. The role of artificial intelligence in hastening time to recruitment in clinical trials. BJR Open. 2023 May 16;5(1):20220023. doi: 10.1259/bjro.20220023.

38. Allam A, Feuerriegel S, Rebhan M, Krauthammer M. Analyzing Patient Trajectories With Artificial Intelligence. J Med Internet Res. 2021 Dec 3;23(12):e29812. doi: 10.2196/29812.

39. Terranova N, Venkatakrishnan K. Machine Learning in Modeling Disease Trajectory and Treatment Outcomes: An Emerging Enabler for Model-Informed Precision Medicine. Clin Pharmacol Ther. 2024 Apr;115(4):720-726. doi: 10.1002/cpt.3153.

40. Birkenbihl C, de Jong J, Yalchyk I, Fröhlich H. Deep learning-based patient stratification for prognostic enrichment of clinical dementia trials. Brain Commun. 2024 Dec 16;6(6):fcae445. doi: 10.1093/braincomms/fcae445.

41. Qiu J, Hu Y, Li L, et al. Deep representation learning for clustering longitudinal survival data from electronic health records. Nat Commun. 2025 Mar 14;16(1):2534. doi: 10.1038/s41467-025-56625-z.

42. Shogenova Z, Krymshokalova DA, Dzhamikhova FKh. Digital patient twin information systems for integrated presentation and processing of medical data. Bulletin of Adyghe State University. Series: Natural, Mathematical, and Technical Sciences. 2025;1:47–54. (In Russ.). doi: 10.53598/2410-3225-2025-1-356-47-54.

43. Bordukova M, Makarov N, Rodriguez-Esteban R, et al. Generative artificial intelligence empowers digital twins in drug discovery and clinical trials. Expert Opin Drug Discov. 2024 Jan-Jun;19(1):33-42. doi: 10.1080/17460441.2023.2273839.

44. Vishnu Priya N. EMA qualifies Unlearn’s AI-driven approach for smaller trials, 2022. Clinical Trials Arena. URL: https://www.clinicaltrialsarena.com/news/ema-qualifies-unlearn-approach/?cf-view. Дата обращения: 15.08.2025.

45. Zhang B, Zhang L, Chen Q, et al. Harnessing artificial intelligence to improve clinical trial design. Commun Med (Lond). 2023 Dec 21;3(1):191. doi: 10.1038/s43856-023-00425-3.

46. Walsh J. Reducing Placebo Burden: TwinRCTs and Their Impact on Clinical Trials. Unlearn.ai Blog. – 2023. [Walsh J. Reducing Placebo Burden: TwinRCTs and Their Impact on Clinical Trials. Unlearn.ai Blog. 2023. Available from: https://www.unlearn.ai/blog/reducing-placebo-burden-twinrcts-and-their-impact-on-clinical-trials. Accessed 2025 Aug 15].

47. Unlearn. European Medicines Agency qualifies Unlearn’s AI-powered method for running smaller, faster clinical trials. // BioSpace. – 2022. [Unlearn. European Medicines Agency qualifies Unlearn’s AI-powered method for running smaller, faster clinical trials. BioSpace. 2022. Available from: https://www.biospace.com/european-medicines-agency-qualifies-unlearn-s-ai-powered-method-for-running-smaller-faster-clinical-trials. Accessed 2025 Aug 15].

48. Luigi Rullo, Paths of Digital twins in the public sector. A systematic review of the social sciences literature. Rivista di Digital Politics. 2024:3;631-53, doi: 10.53227/116592.

49. Chang HC, Gitau AM, Kothapalli S, et al. Understanding the need for digital twins' data in patient advocacy and forecasting oncology. Front Artif Intell. 2023 Nov 10;6:1260361. doi: 10.3389/frai.2023.1260361.

50. Weinberger N, Hery D, Mahr D, et al. Beyond the gender data gap: co-creating equitable digital patient twins. Front Digit Health. 2025 Apr 30;7:1584415. doi: 10.3389/fdgth.2025.1584415.

51. Gelis L, Stoeckert I, Podhaisky HP. Digital Tools-Regulatory Considerations for Application in Clinical Trials. Ther Innov Regul Sci. 2023 Jul;57(4):769-782. doi: 10.1007/s43441-023-00535-z.

52. Sel K, Hawkins-Daarud A, Chaudhuri A, et al. Survey and perspective on verification, validation, and uncertainty quantification of digital twins for precision medicine. NPJ Digit Med. 2025 Jan 17;8(1):40. doi: 10.1038/s41746-025-01447-y.

53. Allen B. The Promise of Explainable AI in Digital Health for Precision Medicine: A Systematic Review. J Pers Med. 2024 Mar 1;14(3):277. doi: 10.3390/jpm14030277.

54. Lampreia F, Madeira C, Dores H. Digital health technologies and artificial intelligence in cardiovascular clinical trials: A landscape of the European space. Digit Health. 2024 Sep 5;10:20552076241277703. doi: 10.1177/20552076241277703.

55. Moglia V, Johnson O, Cook G, et al. Artificial intelligence methods applied to longitudinal data from electronic health records for prediction of cancer: a scoping review. BMC Med Res Methodol. 2025 Jan 28;25(1):24. doi: 10.1186/s12874-025-02473-w.

56. Teodoro D, Naderi N, Yazdani A, et al. A scoping review of artificial intelligence applications in clinical trial risk assessment. NPJ Digit Med. 2025 Jul 30;8(1):486. doi: 10.1038/s41746-025-01886-7.

57. Tong L, Shi W, Isgut M, et al. Integrating Multi-Omics Data With EHR for Precision Medicine Using Advanced Artificial Intelligence. IEEE Rev Biomed Eng. 2024;17:80-97. doi: 10.1109/RBME.2023.3324264.

58. Lipkova J, Chen RJ, Chen B, et al. Artificial intelligence for multimodal data integration in oncology. Cancer Cell. 2022 Oct 10;40(10):1095- 1110. doi: 10.1016/j.ccell.2022.09.012.

59. Azenkot T, Rivera DR, Stewart MD, Patel SP. Artificial Intelligence and Machine Learning Innovations to Improve Design and Representativeness in Oncology Clinical Trials. Am Soc Clin Oncol Educ Book. 2025 Jun;45(3):e473590. doi: 10.1200/EDBK-25-473590.

60. Shoda K, Kawaguchi Y, Maruyama S, Ichikawa D. Essential Updates 2023/2024: Recent Advances of Multimodal Approach in Patients for Gastric Cancer. Ann Gastroenterol Surg. 2025;0:1-9. doi:10.1002/ags3.70041.

61. Goh B, Bhaskar SMM. The role of artificial intelligence in optimizing management of atrial fibrillation in acute ischemic stroke. Ann N Y Acad Sci. 2024 Nov;1541(1):24-36. doi: 10.1111/nyas.15231.

62. Lifebit. AI Driven Drug Discovery: 5 Powerful Breakthroughs in 2025. // Lifebit Blog. – 2025. [Lifebit. AI Driven Drug Discovery: 5 Powerful Breakthroughs in 2025. Lifebit Blog. 2025. Available from: https://lifebit.ai/blog/ai-driven-drug-discovery. Accessed 2025 Aug 17].

63. Cascini F, Beccia F, Causio FA, et al. Scoping review of the current landscape of AI-based applications in clinical trials. Front Public Health. 2022 Aug 12;10:949377. doi: 10.3389/fpubh.2022.949377.

64. Sedano R, Solitano V, Vuyyuru SK, et al. Artificial intelligence to revolutionize IBD clinical trials: a comprehensive review. Therap Adv Gastroenterol. 2025 Feb 23;18:17562848251321915. doi: 10.1177/17562848251321915.

65. Bhask ar SMM. Medicine Meets Science: The Imperative of Scientific Research and Publishing for Physician-Scientists. Indian J Radiol Imaging. 2025 Jan 9;35(Suppl 1):S9-S17. doi: 10.1055/s-0044-1800803.

66. Kobyakova O.S., Eremchenko O.A., Kanev A.A., Kurakova N.G. Quantum technologies in healthcare: analysis of cases and perspectives. Medical Doctor and Information Technologies. 2025;(1):6-21. (In Russ.).

67. Ezeogu FL, Franca MA, Opara J, Palama V, et al. Integrating AI-Based Therapeutic Design and Cloud Cybersecurity for Rare Genetic Diseases: A Systematic Review. Asian J Res Comput Sci. 2025;18(8):43-57. doi:10.9734/ajrcos/2025/v18i8739.

68. Koroleva J.I., Khokhlov A.L., Artemova O.R., Kostina E.V., Zarubina T.V. Code of ethics for the use of artificial intelligence in the Russian Federation healthcare. Medical Doctor and Information Technologies. 2025;(2):98-106. (In Russ.).

69. Khokholov AL, Zarubina TV, Kotlovsky MY, Pavlov AV, Potapov MP, Soldatova ON, et al. Mechanisms for introduction of artificial intelligence in healthcare: new ethical challenges. Medical Ethics. 2024;(3):4–10. (In Russ.).

70. Koshechkin KA, Khokholov AL. Ethical issues in implementing artificial intelligence in healthcare. Medical Ethics. 2024;(1):11–7.

71. Vasiliev Y.A., Gusev A.V., Mikhailova A.A., Sharova D.E., Arzamasov K.M., Vladzymyrskyy A.V. Ethical principles of the development of artificial intelligence systems for healthcare. Medical Doctor and Information Technologies. 2023;(4):36- 41. (In Russ.).

72. Code of Ethics for the Use of Artificial Intelligence in Healthcare. // Portal for operational interaction of participants in the Unified State Health Information System (EGISZ). March 2025. (In Russ.). Доступно по: https://portal.egisz.rosminzdrav.ru/news/1001. Ссылка активна на 23.08.2025.

73. Code of Ethics for the Use of Artificial Intelligence in Healthcare. Version 2.1 (approved by the Interdepartmental Working Group under the Russian Ministry of Health on the Creation, Development, and Implementation of Medical Devices and Services Using Artificial Intelligence Technologies into Clinical Practice, Protocol No. 90/18-0/117 of February 14, 2025). – August 21, 2025. (In Russ.). Доступно по: https://www.garant.ru/products/ipo/prime/doc/411615533/. Ссылка активна на 21.08.2025.

74. Rosenzweig M, Belcher SM, Braithwaite LE, et al. Research Priorities of the Oncology Nursing Society: 2024-2027. Oncol Nurs Forum. 2024 Oct 17;51(6):502-515. doi: 10.1188/24.ONF.502-515.


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Zyryanov S.K., Parshenkov M.A., Yavorskiy A.N. Clinical trial digitalization: new opportunities for the use of artificial intelligence. Kachestvennaya Klinicheskaya Praktika = Good Clinical Practice. 2025;(3):62-72. (In Russ.) https://doi.org/10.37489/2588-0519-2025-3-62-72. EDN: FAVBZS

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