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Transforming Healthcare Delivery for Enhanced Patient Outcomes
Abstract
This paper examines the transformative impact of AI-driven engineering on healthcare delivery for enhanced patient outcomes. Through case studies and research insights, it investigates how artificial intelligence is revolutionizing traditional healthcare practices, including diagnosis, treatment planning, and patient monitoring. The study highlights the application of AI techniques such as deep learning, natural language processing, and medical image analysis in improving accuracy, efficiency, and accessibility in healthcare services. Additionally, it discusses the integration of AI with electronic health records, wearable devices, and telemedicine platforms to enable personalized medicine, remote monitoring, and proactive healthcare management. The paper also addresses challenges such as data privacy, regulatory compliance, and ethical considerations in the adoption of AI-driven engineering solutions in healthcare. It emphasizes the importance of interdisciplinary collaboration, clinician involvement, and patient engagement to harness AI's potential in transforming healthcare delivery and improving population health outcomes.
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References
- Char, D. S., Shah, N. H., Magnus, D., & Atreja, A. (2018). Implementing machine learning in health care—addressing ethical challenges. New England Journal of Medicine, 378(11), 981-983.
- Choi, E., Bahadori, M. T., Schuetz, A., Stewart, W. F., & Sun, J. (2016). Doctor AI: Predicting clinical events via recurrent neural networks. Journal of Machine Learning Research, 17(1), 301-318.
- Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118.
- Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., ... & Kim, R. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA, 316(22), 2402-2410.
- Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358.
- Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56.
- Zhang, J., & Gao, Y. (2019). Artificial intelligence framework for healthcare data integration. IEEE Access, 7, 17769-17778.