Integrating Artificial Intelligence in Healthcare : Improving Patient Care and Diagnosis
Keywords:
Artificial Intelligence, Healthcare, Machine Learning, Predictive Analytics, Patient CareAbstract
Artificial intelligence (AI) has shown great potential in revolutionizing the healthcare sector by enhancing diagnosis accuracy and optimizing treatment plans. This paper investigates the application of AI technologies in healthcare, focusing on machine learning algorithms, natural language processing, and predictive analytics. The study evaluates several case studies where AI has been implemented to assist in early disease detection, personalized treatment, and patient management. Results suggest that AI integration leads to improved healthcare outcomes and efficiency, transforming patient care practices.
References
Char, D. S., Shah, N. H., & Magnus, D. (2018). Implementing machine learning in health care – Addressing ethical challenges. The New England Journal of Medicine, 378(11), 981-983. https://doi.org/10.1056/NEJMp1714229
Choi, E., et al. (2016). Doctor AI: Predicting clinical events via recurrent neural networks. In Proceedings of the 2016 SIAM International Conference on Data Mining.
Esteva, A., et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118. https://doi.org/10.1038/nature21056
Haenlein, M., & Kaplan, A. M. (2019). A brief history of artificial intelligence in healthcare. Business Horizons, 62(5), 687–696. https://doi.org/10.1016/j.bushor.2019.03.003
Jiang, F., et al. (2017). Artificial intelligence in healthcare: Past, present, and future. BMC Medical Informatics and Decision Making, 17(1), 1-5. https://doi.org/10.1186/s12911-017-0475-4
Krittanawong, C., et al. (2017). Artificial intelligence in cardiology: A state-of-the-art review. JACC: Heart Failure, 5(7), 467-474. https://doi.org/10.1016/j.jchf.2017.04.002
Lee, J. H., et al. (2018). Artificial intelligence in healthcare: Past, present, and future. Frontiers in Medicine, 5, 151. https://doi.org/10.3389/fmed.2018.00151
Liu, Y., et al. (2019). Artificial intelligence in healthcare: Past, present and future. Seminars in Cancer Biology, 64, 3-10. https://doi.org/10.1016/j.semcancer.2019.04.006
Mozer, M. C., & Cohn, D. A. (2017). Machine learning for healthcare: On the verge of transformative change. Science, 355(6330), 1091-1095. https://doi.org/10.1126/science.aaf6891
Rajpurkar, P., et al. (2017). CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv preprint arXiv:1711.05225. https://arxiv.org/abs/1711.05225
Razzak, M. I., et al. (2018). Big data analytics for preventive medicine. Computational and Structural Biotechnology Journal, 16, 207–213. https://doi.org/10.1016/j.csbj.2018.01.003
Shickel, B., et al. (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. https://doi.org/10.1109/JBHI.2017.2763106
Topol, E. (2018). The creative destruction of medicine: How the digital revolution will create better health care. Basic Books.
Topol, E. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books.
Zhang, Y., et al. (2020). The application of artificial intelligence in healthcare: A review. International Journal of Environmental Research and Public Health, 17(7), 2473. https://doi.org/10.3390/ijerph17072473
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Proceeding of The International Conference of Inovation, Science, Technology, Education, Children, and Health
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.