Received 01.08.2026, Revised 13.11.2025, Accepted 23.12.2025

Use of intelligent algorithms in virtual healthcare computer systems: From diagnosis to personalised treatment

Mykola Khrulov, Tetiana Myroniuk

The study aimed to theoretically substantiate approaches to the effective implementation of intelligent algorithms in virtual medicine. The methodology was based on theoretical, analytical, and normative-prognostic analysis of the effectiveness and development of intelligent technologies in digital healthcare. The study established that artificial intelligence (AI) is transforming approaches to the collection, analysis and use of medical data. Virtual medicine uses machine learning for diagnosis, prediction and personalised treatment, increasing the accuracy of decisions and reducing the burden on doctors. Machine learning methods are effective for processing electronic medical records and laboratory data, while deep learning forms the basis of virtual medicine by automating the analysis of large amounts of information. Generative models create synthetic medical data and clinical scenarios, supporting the development of personalised medicine and the concept of “digital twins”. Multimodal systems combine different types of data, providing a comprehensive analysis of the patient’s condition and more accurate clinical predictions. The benefits of AI implementation included an 18-25% increase in diagnostic accuracy, a 20-30% reduction in working hours among doctors, expanded access to medicine in remote regions, and lower healthcare costs. The main risks are issues of data security, explainability, ethics, bias, and doctor trust, which necessitate transparency, control, and legal regulation. The European Union has specific legislation that sets requirements for the safety and transparency of medical AI systems, while Ukraine’s regulatory framework is still in the process of being developed. To improve virtual medicine, it is advisable to implement explainable AI, integrate Large Language Models with data protection, apply federated learning, generative simulations and blockchain following ethical and legal standards. The results of the study can be used by specialists when making decisions on the selection and application of intelligent algorithms in medical institutions, research centres, and the IT sphere of healthcare

multimodal medical data analytics; digital monitoring; generative models for simulations; explainability of clinical decisions; telemedicine; security and privacy of medical data
182-194
Khrulov, M., & Myroniuk, T. (2025). Use of intelligent algorithms in virtual healthcare computer systems: From diagnosis to personalised treatment. Information Technologies and Computer Engineering, 22(3), 182-194. https://doi.org/10.31649/vitce/3.2025.182

References

[1] Abbaoui, W., Retal, S., El Bhiri, B., Kharmoum, N., & Ziti, S. (2024). Towards revolutionizing precision healthcare: A systematic literature review of artificial intelligence methods in precision medicine. Informatics in Medicine Unlocked, 46, article number 101475. doi: 10.1016/j.imu.2024.101475.

[2] Abbas, S.R., Seol, H., Abbas, Z., & Lee, S.W. (2025). Exploring the role of artificial intelligence in smart healthcare: A capability and function-oriented review. Healthcare, 13(14), article number 1642. doi: 10.3390/ healthcare13141642.

[3] Alkhanbouli, R., Almadhaani, H.M., Alhosani, F., & Simsekler, M.C. (2025). The role of explainable artificial intelligence in disease prediction: A systematic literature review and future research directions. BMC Medical Informatics and Decision Making, 25, article number 110. doi: 10.1186/s12911-025-02944-6.

[4] Artificial Intelligence Act. (2024,  August). Retrieved from https://eur-lex.europa.eu/legal-content/EN/ TXT/?uri=CELEX%3A32024R1689.

[5] Bathula, A., et al. (2024). Blockchain, artificial intelligence, and healthcare: The tripod of future – a narrative review. Artificial Intelligence Review, 57, article number 238. doi: 10.1007/s10462-024-10873-5.

[6] Boychenko, O., & Bubliy, T. (2024). Prospects for the use of artificial intelligence in the medical field. Current Issues in Modern Medicine: Bulletin of the Ukrainian Medical Stomatological Academy, 24(3), 137-139. doi: 10.31718/20771096.24.3.137.

[7] Cascella, M., Scarpati, G., Bignami, E.G., Cuomo, A., Vittori, A., Di Gennaro, P., Crispo, A., & Coluccia, S. (2023). Utilizing an artificial intelligence framework (conditional generative adversarial network) to enhance telemedicine strategies for cancer pain management. Journal of Anesthesia, Analgesia and Critical Care, 3, article number 19.doi: 10.1186/s44158-023-00104-8.

[8] Clark, H.B., Egger, J., & Duffy, V.G. (2024). AI in healthcare and medicine: A systematic literature review and reappraisal. In V.G. Duffy (Ed.), Digital human modeling and applications in health, safety, ergonomics and risk management. HCII 2024. Lecture notes in computer science (Vol. 14710, pp. 251-270). Cham: Springer. doi: 10.1007/978-3-031-61063-9_17.

[9] El Arab, R.A., & Al Moosa, O.A. (2025). Systematic review of cost effectiveness and budget impact of artificial intelligence in healthcare. npj Digital Medicine, 8, article number 548. doi: 10.1038/s41746-025-01722-y.

[10] European Commission. (2024). Artificial intelligence in healthcare. Retrieved from https://health.ec.europa.eu/ ehealth-digital-health-and-care/artificial-intelligence-healthcare_en.

[11] General Data Protection Regulation. (2016, April). Retrieved from https://gdpr-text.com.

[12] Ghebrehiwet, I., Zaki, N., Damseh, R., & Mohamad, M.S. (2024). Revolutionizing personalized medicine with generative AI: A systematic review. Artificial Intelligence Review, 57, article number 128. doi: 10.1007/s10462-024-10768-5.

[13] Gomez-Cabello, C.A., Borna, S., Pressman, S., Haider, S.A., Haider, C.R., & Forte, A.J. (2024). Artificial-intelligencebased clinical decision support systems in primary care: A scoping review of current clinical implementations. European Journal of Investigative Health Psychology and Education, 14(3), 685-698. doi: 10.3390/ejihpe14030045.

[14] Guo, X., & Li, Y. (2024). Intelligent health in the IS area: A literature review and research agenda. Fundamental Research, 4(4), 961-971. doi: 10.1016/j.fmre.2023.04.008.

[15] Khalifa, M., & Albadawy, M. (2024). AI in diagnostic imaging: Revolutionising accuracy and efficiency. Computer Methods and Programs in Biomedicine Update, 5, article number 100146. doi: 10.1016/j.cmpbup.2024.100146.

[16] Khan, M.M., Shah, N., Shaikh, N., Thabet, A., Alrabayah, T., & Belkhair, S. (2025). Towards secure and trusted AI in healthcare: A systematic review of emerging innovations and ethical challenges. International Journal of Medical Informatics, 195, article number 105780. doi: 10.1016/j.ijmedinf.2024.105780.

[17] Korotka, V.O., & Mokrynskyi, V.A. (2024). Technologies of artificial intelligence in modern medicine: Implementation and issues. Digital Medicine, 163(5), 119-121. doi: 10.32471/umj.1680-3051.163.257497.

[18] Kumah, E. (2025). Artificial intelligence in healthcare and its implications for patient-centered care. Discover Public Health, 22, article number 524. doi: 10.1186/s12982-025-00924-9.

[19] Mennella, C., Maniscalco, U., De Pietro, G., & Esposito, M. (2024). Ethical and regulatory challenges of AI technologies in healthcare: A narrative review. Heliyon, 10(4), article number e26297. doi: 10.1016/j.heliyon.2024.e26297.

[20] Ministry of Digital Transformation of Ukraine. (2023). Regulation of artificial intelligence in Ukraine: Presenting a roadmap. Retrieved from https://thedigital.gov.ua/news/technologies/regulyuvannya-shtuchnogo-intelektu-vukraini-prezentuemo-dorozhnyu-kartu.

[21] Mizna, S., Arora, S., Saluja, P., Das, G., & Alanesi, W.A. (2025). An analytic research and review of the literature on the practice of artificial intelligence in healthcare. European Journal of Medical Research, 30, article number 382.doi: 10.1186/s40001-025-02603-6.

[22] Nilius, H., Tsouka, S., Nagler, M., & Masoodi, M. (2024). Machine learning applications in precision medicine: Overcoming challenges and unlocking potential. TrAC Trends in Analytical Chemistry, 179, article number 117872. doi: 10.1016/j.trac.2024.117872.

[23] Ning, Y., et al. (2024). Generative artificial intelligence and ethical considerations in health care: A scoping review and ethics checklist. The Lancet Digital Health, 6(11), E848-E856. doi: 10.1016/S2589-7500(24)00143-2.

[24] Pool, J., Indulska, M., & Sadiq, S. (2024). Large language models and generative AI in telehealth: A responsible use lens. Journal of the American Medical Informatics Association, 31(9), 2125-2136. doi: 10.1093/jamia/ocae035.

[25] Rasheed, K., Qayyum, A., Ghaly, M., Al-Fuqaha, A., Razi, A., & Qadir, J. (2022). Explainable, trustworthy, and ethical machine learning for healthcare: A survey. Computers in Biology and Medicine, 149, article number 106043. doi: 10.1016/j.compbiomed.2022.106043.

[26] Sadr, H., et al. (2025). Unveiling the potential of artificial intelligence in revolutionizing disease diagnosis and prediction: A comprehensive review of machine learning and deep learning approaches. European Journal of Medical Research, 30, article number 418. doi: 10.1186/s40001-025-02680-7.

[27] Shajari, S., Kuruvinashetti, K., Komeili, A., & Sundararaj, U. (2023). The emergence of AI-based wearable sensors for digital health technology: A review. Sensors, 23(23), article number 9498. doi: 10.3390/s23239498.

[28] Shinde, R., Patil, S., Kotecha, K., Potdar, V., Selvachandran, G., & Abraham, A. (2022). Securing AI-based healthcare systems using blockchain technology: A state-of-the-art systematic literature review and future research directions. ArXiv. doi: 10.48550/arXiv.2206.04793.

[29] Soenksen, L.R., Ma, Y., Zeng, C., Boussioux, L.D., Villalobos Carballo, K., Na, L., Wiberg, H.M., Li, M.L., Fuentes, I., & Bertsimas, D. (2022). Integrated multimodal artificial intelligence framework for healthcare applications. ArXiv. doi: 10.48550/arXiv.2202.12998.

[30] Sofilkanych, N., Vesova, O., Kaminskyy, V., & Kryvosheieva, A. (2023). The impact of artificial intelligence on Ukrainian medicine: Benefits and challenges for the future. Futurity Medicine, 2(4), 28-39. doi: 10.57125/FEM.2023.12.30.04.

[31] Tang, L., Li, J., & Fantus, S. (2023). Medical artificial intelligence ethics: A systematic review of empirical studies. Digital Health, 9, 1-22. doi: 10.1177/20552076231186064.

[32] Thapa, S., Fakiraswamimath, A.P., Zuluaga, M., Kumar, A.R., Ramesh, K., & Yadav, S. (2024). The role of artificial intelligence in personalized medicine: Current trends and future directions. Frontiers in Health Informatics, 13(3), 3830-3841.

[33] van Kolfschooten, H., & van Oirschot, J. (2024). The EU Artificial Intelligence Act (2024): Implications for healthcare. Health Policy, 149, article number 105152. doi: 10.1016/j.healthpol.2024.105152.