Citation:

Praveen Borra, Harshavardhan Nerella, 2024. AI Revolution in Healthcare: AWS Innovations and Future Directions, ESP Journal of Engineering & Technology Advancements  4(3): 23-27.

Abstract:

Artificial Intelligence (AI) integration into healthcare has revolutionized patient care, operational efficiency, and medical research. Amazon Web Services (AWS), a leader in cloud computing, has been pivotal in advancing AI technologies to address critical healthcare challenges. This paper explores the innovations, applications, benefits, and future potential of AWS AI in transforming healthcare delivery. AWS AI encompasses diverse applications that streamline healthcare processes. Particularly impactful is its role in medical imaging analysis. AWS AI solutions like Amazon Rekognition and Amazon Comprehend Medical enable healthcare providers to analyze radiological images and pathology slides accurately and swiftly, facilitating faster diagnoses and personalized treatment plans. Beyond diagnostics, AWS AI supports predictive analytics and patient monitoring. Machine learning algorithms predict patient deterioration, optimize resource allocation, and enable proactive interventions, reducing hospital readmissions and enhancing patient safety.

AWS AI also enhances administrative functions in healthcare. Solutions such as Amazon Transcribe and Amazon Translate improve medical transcription accuracy and enable multilingual patient communication, easing administrative burdens on healthcare professionals. Moreover, AWS AI drives advancements in drug discovery and clinical research by leveraging scalable infrastructure and machine learning capabilities. Researchers analyze vast datasets to identify drug targets, accelerate clinical trials, and customize treatments for patients. Looking ahead, AWS AI promises to revolutionize virtual health assistants, genomics, and population health management. As AI evolves within AWS's secure and scalable cloud platform, it is poised to redefine healthcare delivery globally, offering personalized, efficient, and accessible solutions that enhance patient outcomes.

References:

[1] Amazon Web Services (2023). Healthcare and life sciences solutions. Retrieved from https://aws.amazon.com/health/

[2] Amazon Web Services. (2023). AWS Artificial Intelligence. Retrieved from https://aws.amazon.com/ai/

[3] Feng, M., Li, Y., & Liu, X. (2021). Application of AI technology in medical image diagnosis based on AWS. Journal of Medical Imaging and Health Informatics, 11(5), 1200-1207.

[4] Rajkomar, A., Oren, E., Chen, K., et al. (2018). Scalable and accurate deep learning with electronic health records. NPJ Digital Medicine, 1(1), Article 18.

[5] Hsieh, J., Yao, B., Chen, K., et al. (2020). Predicting patient admission rates using machine learning models on AWS. Healthcare Informatics Research, 26(1), 56-62.

[6] Jiang, F., Jiang, Y., Zhi, H., et al. (2019). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 4(3), 230-243.

[7] Shickel, B., Tighe, P. J., Bihorac, A., 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.

[8] Esteva, A., Kuprel, B., Novoa, R. A., et al. (2019). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118.

[9] Ting, D. S. W., Pasquale, L. R., Peng, L., et al. (2020). Artificial intelligence and deep learning in ophthalmology. British Journal of Ophthalmology, 104(3), 301-306.

[10] 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.

[11] Ngiam, K. Y., Khor, I. W., & Lee, Y. S. (2018). Big data and machine learning algorithms for health-care delivery. Lancet Oncology, 19(5), e262-e273.

[12] Car, J., Sheikh, A., Wicks, P., et al. (2018). Beyond the hype of big data and artificial intelligence: Building foundations for knowledge and wisdom. BMC Medicine, 16(1), Article 157.

[13] Wang, F., Casalino, L. P., & Khullar, D. (2021). Deep learning in medicine - Promise, progress, and challenges. JAMA Internal Medicine, 181(3), 293-294.

[14] Amazon Web Services (2023). AWS Blog Home, https://aws.amazon.com/blogs/industries/enabling-efficient-patient-care-using-amazon-ai-services/

[15] "DIFFERENTIAL PRIVACY TECHNIQUES IN MACHINE LEARNING FOR ENHANCED PRIVACY PRESERVATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 2, page no.b148-b153, February-2024, Available: http://www.jetir.org/papers/JETIR2402116.pdf

Keywords:

AWS, Artificial Intelligence, Healthcare, Cloud Computing, Machine Learning, Medical Imaging, Clinical Decision Support Systems, Drug Discovery, Predictive Analytics, HIPAA.