CHALLENGES RELATED TO THE IMPLEMENTATION OF ARTIFICIAL INTELLIGENCE IN THE HEALTHCARE SYSTEM
DOI:
https://doi.org/10.11603/mie.1996-1960.2025.1-2.16051Keywords:
artificial intelligence, healthcare, mHealth, personalized medicine, medical data, ethical challengesAbstract
Abstract. Background. The rapid development of artificial intelligence technologies has significantly influenced modern healthcare, particularly in the context of personalized medicine and mobile health (mHealth). Artificial intelligence enables the analysis of large volumes of medical data, supports clinical decision-making, and improves the management of chronic diseases. However, despite its growing potential, the integration of artificial intelligence into healthcare systems is associated with a number of technological, organizational, and ethical challenges that require comprehensive analysis.
Materials and Methods. The study is based on a theoretical analysis of scientific publications and systematic reviews devoted to the application of artificial intelligence in healthcare. Data were collected and systematized using leading scientometric databases, including Scopus, PubMed, and ScienceDirect. General scientific methods of information retrieval, comparison, analysis, and synthesis were applied to identify key challenges and risks related to the implementation of artificial intelligence technologies in medical practice.
Results. The analysis identified major challenges in the implementation of artificial intelligence in healthcare, including issues of data privacy and security, lack of standardized approaches to evaluating digital health technologies, limited reproducibility of results, and insufficient generalizability of models. Additional concerns include high computational requirements, variability and heterogeneity of medical data, and the need for interdisciplinary integration.
Conclusions. The effective integration of artificial intelligence into healthcare is possible only through overcoming key technological and ethical barriers. The development of standardized data processing protocols, implementation of regulatory frameworks, use of synthetic data, and training of healthcare professionals are essential steps toward the large-scale adoption of artificial intelligence-based solutions.
References
Liu, Y. C., Chen, C. H., Lee, C. W., Lin, Y. S., Chen, H. Y., Yeh, J. Y., et al. (2016). Design and usability evaluation of user-centered and visual- based aids for dietary food measurement on mobile devices in a randomized controlled trial. Journal of Biomedical Informatics, 64, 122–130. doi: 10.1016/j.jbi.2016.10.001.
Liu, Y. C., Wu, S. T., Lin, S. J., Chen, C. H., Lin, Y. S., Chen, H. Y. (2020). Usability of food size aids in mobile dietary reporting apps for young adults: randomized controlled trial. JMIR mHealth and uHealth, 8(4), e14543. doi: 10.2196/14543.
Liu, Y. C., Chen, C. H., Lin, Y. S., Chen, H. Y., Irianti, D., Jen, T. N., et al. (2020). Design and usability evaluation of mobile voice-added food reporting for elderly people: randomized controlled trial. JMIR mHealth and uHealth, 8(9), e20317. doi: 10.2196/20317.
Mauch, C. E., Laws, R. A., Prichard, I., Maeder, A. J., Wycherley, T. P., Golley, R. K. (2021). Commercially available apps to support healthy family meals: user testing of app utility, acceptability, and engagement. JMIR mHealth and uHealth, 9(5), e22990. doi: 10.2196/22990.
Gordon, W. J., Landman, A., Zhang, H., Bates, D. W. (2020). Beyond validation: getting health apps into clinical practice. npj Digital Medicine, 3, 14. doi: 10.1038/s41746-019-0212-z.
Kosa, S. D., Monize, J., D'Souza, M., Joshi, A., Philip, K., Reza, S., et al. (2018). Nutritional mobile applications for CKD patients: systematic review. Kidney International Reports, 4(3), 399–407. doi: 10.1016/j.ekir.2018.11.016.
Zhou, L., Bao, J., Setiawan, I. M. A., Saptono, A., Parmanto, B. (2019). The mHealth app usability questionnaire (MAUQ): development and validation study. JMIR mHealth and uHealth, 7(4), e11500. doi: 10.2196/11500.
Johnson, K. B., Wei, W. Q., Weeraratne, D., Frisse, M. E., Misulis, K., Rhee, K., et al. (2021). Precision medicine, artificial intelligence, and the future of personalized health care. Clinical and Translational Science, 14(1), 86–93. doi: 10.1111/cts.12884.
Deniz-Garcia, A., Fabelo, H., Rodriguez-Almeida, A. J., Zamora-Zamorano, G., Castro-Fernandez, M., Alberiche Ruano, M. D. P., et al. (2023). Quality, usability, and effectiveness of mHealth apps and the role of artificial intelligence: current scenario and challenges. Journal of Medical Internet Research, 25, e44030. doi: 10.2196/44030.
Seetharam, K., Kagiyama, N., Sengupta, P. P. (2019). Application of mobile health, telemedicine and artificial intelligence to echocardiography. Echo Research and Practice, 6(2), R41–R52. doi: 10.1530/ERP-18-0081.
Han, S. S., Park, I., Eun Chang, S., Lim, W., Kim, M. S., Park, G. H., et al. (2020). Augmented intelligence dermatology: deep neural networks empower medical professionals in diagnosing skin cancer and predicting treatment options for 134 skin disorders. Journal of Investigative Dermatology, 140(9), 1753–1761. doi: 10.1016/j.jid.2020.01.019.
Basu, S., Johnson, K. T., Berkowitz, S. A. (2020). Use of machine learning approaches in clinical epidemiological research of diabetes. Current Diabetes Reports, 20(12), 80. doi: 10.1007/s11892-020-01353-5.
Dagliati, A., Marini, S., Sacchi, L., Cogni, G., Teliti, M., Tibollo, V., et al. (2018). Machine learning methods to predict diabetes complications. Journal of Diabetes Science and Technology, 12(2), 295–302. doi: 10.1177/1932296817706375.
Akter, L., Al-Islam, F. (2022). Diabetes mellitus prediction and feature importance score finding using extreme gradient boosting. In: Proceedings of the International Conference on Emerging Technologies and Intelligent Systems (ICETIS 2021). Lecture Notes in Networks and Systems, 322, 643–654. doi: 10.1007/978-3-030-85990-9_52.
Li, J., Chen, Q., Hu, X., Yuan, P., Cui, L., Tu, L., et al. (2021). Establishment of noninvasive diabetes risk prediction model based on tongue features and machine learning techniques. International Journal of Medical Informatics, 149, 104429. doi: 10.1016/j.ijmedinf.2021.104429.
Zhang, S., Bamakan, S. M. H., Qu, Q., Li, S. (2019). Learning for personalized medicine: a comprehensive review from a deep learning perspective. IEEE Reviews in Biomedical Engineering, 12, 194–208. doi: 10.1109/RBME.2018.2864254.
Claus, J., Hanley, L. C., Ou, J. (2024). General pathology student guide: with AMBOSS shortcuts. Heidelberg: Springer. doi: 10.1007/978-3-662-67962-3.
Contreras, I., Vehi, J. (2018). Artificial intelligence for diabetes management and decision support: literature review. Journal of Medical Internet Research, 20(5), e10775. doi: 10.2196/10775.
Goyal, M., Knackstedt, T., Yan, S., Hassanpour, S. (2020). Artificial intelligence-based image classification methods for diagnosis of skin cancer: challenges and opportunities. Computers in Biology and Medicine, 127, 104065. doi: 10.1016/j.compbiomed.2020.104065.
Saginala, K., Barsouk, A., Aluru, J. S., Rawla, P., Barsouk, A. (2021). Epidemiology of melanoma. Medical Sciences, 9(4), 63. doi: 10.3390/medsci9040063.
Kho, J., Gillespie, N., Horsham, C., Snoswell, C., Vagenas, D., Soyer, H. P., et al. (2020). Skin doctor consultations using mobile teledermoscopy: exploring virtual care business models. Telemedicine and e-Health, 26(11), 1406–1413. doi: 10.1089/tmj.2019.0228.
Knight, S. R., Ng, N., Tsanas, A., Mclean, K., Pagliari, C., Harrison, E. M. (2021). Mobile devices and wearable technology for measuring patient outcomes after surgery: a systematic review. npj Digital Medicine, 4(1), 157. doi: 10.1038/s41746-021-00525-1.
Gunasekeran, D. V., Tseng, R. M. W. W., Tham, Y. C., Wong, T. Y. (2021). Applications of digital health for public health responses to COVID-19: a systematic scoping review of artificial intelligence, telehealth and related technologies. npj Digital Medicine, 4(1), 40. doi: 10.1038/s41746-021-00412-9.
Goncalves, A., Ray, P., Soper, B., Stevens, J., Coyle, L., Sales, A. P. (2020). Generation and evaluation of synthetic patient data. BMC Medical Research Methodology, 20(1), 108. doi: 10.1186/s12874-020-00977-1.
Reiner Benaim, A., Almog, R., Gorelik, Y., Hochberg, I., Nassar, L., Mashiach, T., et al. (2020). Analyzing medical research results based on synthetic data and their relation to real data results: systematic comparison from five observational studies. JMIR Medical Informatics, 8(2), e16492. doi: 10.2196/16492.
Rodriguez-Almeida, A. J., Fabelo, H., Ortega, S., Deniz, A., Balea-Fernandez, F. J., Quevedo, E., et al. (2023). Synthetic patient data generation and evaluation in disease prediction using small and imbalanced datasets. IEEE Journal of Biomedical and Health Informatics, 27(6), 2670–2680. doi: 10.1109/ JBHI.2022.3196697.
Dascalu, A., Walker, B. N., Oron, Y., David, E. O. (2022). Non-melanoma skin cancer diagnosis: a comparison between dermoscopic and smartphone images by unified visual and sonification deep learning algorithms. Journal of Cancer Research and Clinical Oncology, 148(9), 2497–2505. doi: 10.1007/s00432- 021-03809-x.
World Health Organization. (2021). Ethics and governance of artificial intelligence for health. Geneva: World Health Organization.
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