AI in Healthcare for Diagnosis and Treatment
DOI:
https://doi.org/10.61841/q1h4k604Keywords:
AI, healthcare, diagnosis, treatment, machine learning, deep learning, personalized medicine, precision therapeutics, medical imagingAbstract
Artificial intelligence (AI) is emerging as a transformative technology with tremendous potential to transform healthcare systems, especially research and treatment processes This paper explores the various applications and implications of the ways in which AI plays a role in healthcare; to deliver increased assessment accuracy, personalized treatment plans and improved patient outcomes confirm their role. AI-based diagnostic tools analyze complex medical data using machine learning algorithms, deep learning models, and natural language processing techniques, ranging from medical images and electronic health records to genomic sequences. In addition, AI-enabled medicine approaches include personalized medicine and precision medicine, the development of treatment regimens based on individual patient characteristics, genetic profiles, and reactions This development contributes to better diagnosis, recommends targeted therapies, and maximizes therapeutic effectiveness against side effects.
However, integrating AI into healthcare is not without its challenges. Issues such as data privacy, interpretation of AI generated insights, regulatory compliance, and ethical considerations present significant hurdles that require careful navigation and regulatory processes.
Looking ahead, the future of AI in health research and treatment includes continuous improvements in AI algorithms, collaborative health systems, collaborative efforts of health professionals and engineers, and ethics addressing concerns, ensuring transparency, and enhancing the regulatory framework. Maintaining patient confidence and safety is essential to realizing their full potential.
In conclusion, AI-powered technology holds tremendous promise to reshape healthcare delivery by improving diagnostic accuracy, optimizing treatment options, and ultimately taking off patient care effectiveness of. Embracing the power of AI while addressing the challenges is critical to paving the way for more efficient, personalized, and accessible healthcare.
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1. Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., ... & Corrado, G. (2017). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24-29.
2. Ting, D. S. W., Carin, L., Dzau, V., & Wong, T. Y. (2019). Digital technology and COVID-19. Nature Medicine, 26(4), 459-461.
3. 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.
4. Beam, A. L., & Kohane, I. S. (2018). Big data and machine learning in health care. JAMA, 319(13), 1317-1318.
5. Char, D. S., Shah, N. H., Magnus, D., Zelenev, A., Mandl, K. D., & Mandel, J. C. (2018). Implementing machine learning in health care—Addressing ethical challenges. New England Journal of Medicine, 378(11), 981-983.
6. Davenport, T., Kalakota, R., & Dreyer, K. (2019). Putting AI to work in health care. Harvard Business Review, 97(1), 100-111.
7. 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.
8. Krittanawong, C., Zhang, H., Wang, Z., Aydar, M., & Kitai, T. (2017). Artificial intelligence in precision cardiovascular medicine. Journal of the American College of Cardiology, 69(21), 2657-2664.
9. Hinton, G. E., & LeCun, Y. (2018). Deep learning—The black box of AI in medicine. Nature Biomedical Engineering, 2(1), 1-3.
10. Rajpurkar, P., Irvin, J., Ball, R. L., Zhu, K., Yang, B., Mehta, H., ... & Ng, A. Y. (2018). Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLOS Medicine, 15(11), e1002686.
11. R. K. Kaushik Anjali and D. Sharma, "Analyzing the Effect of Partial Shading on Performance of Grid Connected Solar PV System", 2018 3rd International Conference and Workshops on Recent Advances and Innovations in Engineering (ICRAIE), pp. 1-4, 2018.
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