AI in Healthcare: Revolutionizing Diagnosis, Treatment, and Patient Care Through Machine Learning
DOI:
https://doi.org/10.36676/dira.v13.i1.162Keywords:
Artificial Intelligence (AI), Healthcare, Machine Learning, Diagnosis, TreatmentAbstract
Artificial intelligence (AI) is supporting advances in healthcare diagnosis, treatment, and patient care through the application of machine learning techniques, which is causing a revolution in the sector. Artificial intelligence's (AI) revolutionary impact on healthcare is explored in this research. Machine learning algorithms are changing traditional medical practices and improving patient outcomes; this is the main emphasis of the research. Artificial intelligence algorithms can analyse large amounts of medical data, including EHRs, MRIs, and genetic information, to spot patterns, find outliers, and provide tailored insights to doctors. Healthcare services may be delivered more efficiently, accurately, and conveniently with the help of AI-driven technologies. Among these options are precision medicine, remote patient monitoring, and early disease detection. To ensure the responsible deployment of AI-driven advancements and equitable access to these technologies, it is necessary to appropriately address the various ethical, legal, and socio-economic problems surrounding the use of AI in healthcare. There is much hope that AI will improve people's lives and completely alter the way healthcare is provided. It is possible that this might be achieved if healthcare practitioners, researchers, lawmakers, and technologists worked together.
References
Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., ... & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24-29.
Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56.
Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358.
Beam, A. L., & Kohane, I. S. (2018). Big data and machine learning in health care. JAMA, 319(13), 1317-1318.
Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future—big data, machine learning, and clinical medicine. New England Journal of Medicine, 375(13), 1216-1219.
Ting, D. S., Peng, L., Varadarajan, A. V., Keane, P. A., Burlina, P. M., Chiang, M. F., ... & Wong, T. Y. (2019). Deep learning in ophthalmology: the technical and clinical considerations. Progress in Retinal and Eye Research, 72, 100759.
Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2018). Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE journal of biomedical and health informatics, 22(5), 1589-1604.
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., ... & Dieleman, S. (2016). Mastering the game of Go with deep neural networks and tree search. nature, 529(7587), 484-489.
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., ... & Webster, D. R. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Jama, 316(22), 2402-2410.
Beam, A. L., & Kohane, I. S. (2018). Big data and machine learning in health care. JAMA, 319(13), 1317-1318.
Downloads
Published
How to Cite
License
Copyright (c) 2025 Darpan International Research Analysis
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
