Studying The Features Of Artificial Intelligence In Early Diagnosis Of Diseases
DOI:
https://doi.org/10.37547/ijmscr/Volume06Issue02-06Keywords:
Artificial intelligence, Early diagnosis, Machine learningAbstract
Through machine learning algorithms, neural networks, and big data analysis, AI systems can detect subtle patterns in medical images, laboratory results, and clinical data that may escape human observation. Artificial intelligence (AI) has become an essential tool in modern healthcare, offering innovative solutions for early disease diagnosis. Early detection of diseases such as cancer, diabetes, cardiovascular disorders, and neurodegenerative conditions has significantly improved through AI-assisted diagnostic tools. These technologies not only enhance diagnostic accuracy and speed but also reduce healthcare costs and support personalized treatment plans. However, the integration of AI into medical diagnostics requires careful consideration of data privacy, algorithm transparency, and ethical implications. Overall, AI continues to revolutionize early disease diagnosis and holds immense promise for the future of precision medicine.
References
Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G. S., Thrun, S., & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29. https://doi.org/10.1038/s41591-018-0316-z
Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230–243. https://doi.org/10.1136/svn-2017-000101
Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine, 17(1), 195. https://doi.org/10.1186/s12916-019-1426-2
Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018). Deep learning for healthcare: Review, opportunities and challenges. Briefings in Bioinformatics, 19(6), 1236–1246. https://doi.org/10.1093/bib/bbx044
Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94–98. https://doi.org/10.7861/futurehosp.6-2-94
Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347–1358. https://doi.org/10.1056/NEJMra1814259
Hamet, P., & Tremblay, J. (2017). Artificial intelligence in medicine. Metabolism: Clinical and Experimental, 69, S36–S40. https://doi.org/10.1016/j.metabol.2017.01.011
Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future—big data, machine learning, and clinical medicine. The New England Journal of Medicine, 375(13), 1216–1219. https://doi.org/10.1056/NEJMp1606181
Yu, K. H., Beam, A. L., & Kohane, I. S. (2018). Artificial intelligence in healthcare. Nature Biomedical Engineering, 2(10), 719–731. https://doi.org/10.1038/s41551-018-0305-z
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Voitova Gavkhar Alisherovna
This work is licensed under a Creative Commons Attribution 4.0 International License.
