SUMMARY
The article provides a comprehensive analysis of the digital transformation of diagnostic processes in modern healthcare, the information and communication technology (ICT) infrastructure in this field, and the application possibilities of artificial neural networks. The relevance of the research stems from the necessity of analyzing large volumes of visual and digital data generated daily in medical institutions with high accuracy. The main objective of the work is to identify optimal mathematical models that ensure the integration of clinical diagnostics with modern technological solutions. The paper examines technical procedures such as pre-processing of medical images, noise reduction and segmentation, as well as the role of Convolutional Neural Networks in diagnostic accuracy. During the research, mathematical metrics such as sensitivity and specificity were used as the basis for evaluating the effectiveness of neural networks, and the system's ability to minimize false negative results was particularly emphasized. Applying an interdisciplinary approach, the prospects of artificial intelligence in early detection of diseases in fields such as radiology, oncology and ophthalmology are demonstrated. In conclusion, it is substantiated that intelligent systems are not intended to replace physicians, but serve as an effective "second opinion" tool that reduces their workload by 30-40% and minimizes subjective errors in decision-making. The multimodal analysis capabilities of modern ICT infrastructure pave the way for the creation of a "unified digital patient profile" and the development of personalized medicine in the future.
Keywords: ICT infrastructure, artificial intelligence, neural networks, diagnostic systems, medical data, digital healthcare

---

REFERENCES

1. Əlizadə M.N., Musayev İ.K., Əliyev E.B. Müasir informasiya sistemlərinin idarə olunması (dərslik). Bakı, “MSV” nəşriyyatı, 2016, 237 s.
2. Li Z, Wang L, Wu X, Jiang J, et al. Artificial intelligence in ophthalmology: The path to the real-world clinic // Cell Rep Med. 2023 Jul 18;4(7):101095. doi: 10.1016/j.xcrm.2023.101095.
3. Əlizadə M.N., Bayramov H.M., Məmmədov Ə.S. İnformasiya təhlükəsizliyi (dərslik). Bakı, “İqtisad universiteti” nəşriyyatı, 2016, 384 s.
4. Mazurowski MA, Buda M, Saha A, Bashir MR. Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI // J Magn Reson Imaging. 2019 Apr;49(4):939-954. doi: 10.1002/jmri.26534.
5. LeCun Y, Bengio Y, Hinton G. Deep learning // Nature. 2015 May 28;521(7553):436-44. doi: 10.1038/nature14539.
6. Esteva A, Robicquet A, Ramsundar B, Kuleshov V, et al. A guide to deep learning in healthcare // Nat Med. 2019 Jan;25(1):24-29. doi: 10.1038/s41591-018-0316-z
7. Costin H.-N, Fira M, Goraș L. Artificial Intelligence in Ophthalmology: Advantages and Limits // Appl. Sci. 2025, 15, 1913. doi: 10.3390/app15041913.
8. Hosny A, Parmar C, Quackenbush J, Schwartz LH, et al. Artificial intelligence in radiology // Nat Rev Cancer. 2018 Aug;18(8):500-510. doi: 10.1038/s41568-018-0016-5.