AI-Enhanced Virtual Assistants for Healthcare Support and Patient Monitoring
Abstract
Virtual assistants powered by AI can significantly enhance healthcare delivery by providing continuous patient support and monitoring. This paper introduces an AI-enhanced virtual assistant designed to assist healthcare professionals and patients in managing chronic conditions. The system integrates natural language processing and machine learning to provide personalized health advice, medication reminders, and real-time monitoring of patient vitals. Evaluations on patient datasets demonstrate improved adherence to treatment plans and better health outcomes. This research highlights the role of AI in supporting both patients and healthcare providers in managing long-term health conditions.
References
Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798-1828.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877-1901.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 770-778).
Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527-1554.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780.
Kingma, D. P., & Ba, J. (2015). Adam: A method for stochastic optimization. In 3rd International Conference on Learning Representations (ICLR) (pp. 1-15).
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems (pp. 1097-1105).
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. In Proceedings of the International Conference on Learning Representations (ICLR).
Murphy, K. P. (2012). Machine learning: A probabilistic perspective. MIT Press.
Ng, A. Y., & Jordan, M. I. (2002). On discriminative vs. generative classifiers: A comparison of logistic regression and naive Bayes. In Advances in Neural Information Processing Systems (pp. 841-848).
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533-536.
Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85-117.
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., ... & Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484-489.
Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). MIT Press.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems (pp. 5998-6008).
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600-612.
Williams, R. J. (1992). Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8(3-4), 229-256.
Chintale, P., Korada, L., Ranjan, P., & Malviya, R. K. (2019). Adopting Infrastructure as Code (IaC) for Efficient Financial Cloud Management. ISSN: 2096-3246, 51(04).
Chintale, P., Korada, L., WA, L., Mahida, A., Ranjan, P., & Desaboyina,(2020) G. RISK MANAGEMENT STRATEGIES FOR CLOUD-NATIVE FINTECH APPLICATIONS DURING THE PANDEMIC.
Dahiya, S., Singh, S. K., Choudhary, S. K., Ranjan, P., & Cognizant, N. J. (2020). FUNDAMENTALS OF DIGITAL TRANSFORMATION IN FINANCIAL SERVICES: KEY DRIVERS AND STRATEGIES. Han, X., Zhao, X., de Almeida, AL, Freitas, WDC, & Bai, W, 1655-1659.
Raghunath, V., Kunkulagunta, M., & Nadella, G. S. (2020). Optimizing SAP Data Processing with Machine Learning Algorithms in Cloud Environments. International Transactions in Artificial Intelligence, 4(4).
Raghunath, V., Kunkulagunta, M., & Nadella, G. S. (2020). Artificial Intelligence in Business Analytics: Cloud-Based Strategies for Data Processing and Integration. International Journal of Sustainable Development in Computing Science, 2(4).
Raghunath, V., Kunkulagunta, M., & Nadella, G. S. (2020). Scalable Data Processing Pipelines: The Role of AI and Cloud Computing. International Scientific Journal for Research, 2(2).
Raghunath, V., Kunkulagunta, M., & Nadella, G. S. (2021). Leveraging Cloud Computing for Efficient Data Processing in SAP Enterprise Solutions. International Journal of Machine Learning for Sustainable Development, 3(4).
Raghunath, V., Kunkulagunta, M., & Nadella, G. S. (2021). Machine Learning in SAP Workflows: A Study of Predictive Analytics and Automation. Transactions on Latest Trends in Artificial Intelligence, 2(2).
Raghunath, V., Kunkulagunta, M., & Nadella, G. S. (2021). Machine Learning Models for Optimizing SAP-Based Data Processing in Cloud Environments. International Journal of Sustainable Development in Computing Science, 3(3).
Ranjan, P., & Dahiya, S. (2021). Advanced threat detection in api security: Leveraging machine learning algorithms. International Journal of Communication Networks and Information Security, 13(1).
Dhaiya, S., Pandey, B. K., Adusumilli, S. B. K., & Avacharmal, R. (2021) Optimizing API Security in FinTech Through Genetic Algorithm based Machine Learning Model.
Singh, S. K., Choudhary, S. K., Ranjan, P., Cognizant, N. J., & Dahiya, S. (2022) COMPARATIVE ANALYSIS OF MACHINE LEARNING MODELS AND DATA ANALYTICS TECHNIQUES FOR FRAUD DETECTION IN BANKING SYSTEM.
Ranjan, P., Khunger, A., Satya, C. B. V. V., & Dahiya, S. (2022) Threat Modeling and Risk Assessment of APIs in Fintech Applications.
Raghunath, V., Kunkulagunta, M., & Nadella, G. S. (2022). Advanced Business Analytics Using Machine Learning and Cloud-Based Data Integration. International Scientific Journal for Research, 4(4).
Raghunath, V., Kunkulagunta, M., & Nadella, G. S. (2023). AI-Driven Business Analytics Framework for Data Integration Across Hybrid Cloud Systems. Transactions on Latest Trends in Artificial Intelligence, 4(4).
Raghunath, V., Kunkulagunta, M., & Nadella, G. S. (2023). Integrating AI and Cloud Computing for Scalable Business Analytics in Enterprise Systems. International Journal of Sustainable Development in Computing Science, 5(3).
Raghunath, V., Kunkulagunta, M., & Nadella, G. S. (2023). Enhancing Data Integration Using AI and ML Techniques for Real-Time Analytics. International Journal of Machine Learning for Sustainable Development, 5(3).
Ranjan, P., Dahiya, S., Singh, S. K., & Choudhary, S. K. (2023) ENHANCING STOCK PRICE PREDICTION: A COMPREHENSIVE ANALYSIS UTILIZING MACHINE LEARNING AND DEEP LEARNING APPROACHES.
Banerjee, P., Roy, R., Batchu, C., & Ranjan, P. (2023) Examining the Application of Data Federation across Cloud Databases in the Financial Services Domain.
Raghunath (2024), "Security Issues Analysis Based on Big Data in Cloud Computing," World Journal of Advanced Research and Reviews, vol. 23, no. 3, pp. 2549-2557, 2024.
Raghunath (2024), "Analysis on Addressing the Threats to Cloud Computing on the Basis of Security Safeguards for SAP Cloud Services," World Journal of Advanced Research and Reviews, vol. 23, no. 3, pp. 2539-2548, 2024.