AI-Enhanced Intelligent Healthcare: Advancements in Remote Monitoring, Predictive Analytics and Disease Diagnosis

Gburi, Hussein; Ali, Abeer

doi:10.25673/121018

Proceedings of International Conference on Applied Innovation in IT · 2025/07/26 · Vol. 13 · Issue 3 · pp. 243–251

AI-Enhanced Intelligent Healthcare: Advancements in Remote Monitoring, Predictive Analytics and Disease Diagnosis

Hussein Qahtan Al Gburi, Abeer Tariq Maolood and Akbas Ezaldeen Ali

📄 Download PDF DOI: 10.25673/121018

Abstract

Artificial Intelligence (AI) and Deep Learning (DL) in healthcare have brought advanced health monitoring and predictive analytics. By amalgamating AI with the IoT, Remote Healthcare Monitoring (RHM) systems have been generated, allowing the constant monitoring of patients’ health statuses along with decreasing healthcare expenditures. In this paper, we analyze AI-based healthcare innovations exerting their impact in modern medical practices and the need for more advancement in the healthcare predictive solutions from the perspective of AI and the role AI plays in healthcare, that is, the applications of AI in healthcare like disease diagnosis, biomedical research, and predictive analytics. Weather Clinical Decision Support Systems (CDSS) use AI to make specific therapy suggestions based on patient’s characteristics and facilitate the best outcomes. Additionally, AI smart city healthcare systems provide effective solutions for remote applications and in improving smart city urban healthcare services. There have been recent advancements in deep learning being used to accurately identify deep learning through Convolutional Neural Networks (CNNs) and predictive modeling of chronic diseases. While AI increases healthcare efficiency and precision to a great extent, existing issues such as data privacy, model interpretability, and system integration still exist. Further studies should focus on further improving natural language processing (NLP) and the adaptability of the AI models along with looking into ethical issues before the widespread usage of AI can be allowed.

Keywords

Artificial Intelligence Deep Learning CNN Machine Learning Internet of Things Healthcare.

References

N. Wu and Y. Xie, “A survey of machine learning for computer architecture and systems,” arXiv preprint arXiv:2102.07952, 2021. [Online]. Available: https://arxiv.org/abs/2102.07952.
D. Das, P. Raghavendra, and A. Ramachandran, “SPECNet: Predicting SPEC scores using deep learning,” in Proc. Companion ACM/SPEC Int. Conf. Performance Engineering, Apr. 2018, pp. 29–32.
Y. Wang, V. Lee, G.-Y. Wei, and D. Brooks, “Predicting new workload or CPU performance by analyzing public datasets,” ACM Transactions on Architecture and Code Optimization, vol. 15, no. 4, pp. 1–21, Jan. 2019.
B. Ozisikyilmaz, G. Memik, and A. Choudhary, “Machine learning models to predict performance of computer system design alternatives,” in Proc. 37th Int. Conf. Parallel Processing, Sep. 2008, pp. 495–502.
A. Tousi and C. Zhu, “Arm research starter kit: System modeling using gem5,” Arm Research, U.K., Jul. 2017.
J. Bucek, K.-D. Lange, and J. V. Kistowski, “SPEC CPU2017: Next-generation compute benchmark,” in Proc. ACM/SPEC Int. Conf. Performance Engineering, 2018, pp. 41–42.
F. Pedregosa, “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, Oct. 2011.
H. Borchani, G. Varando, C. Bielza, and P. Larrañaga, “A survey on multi-output regression,” Data Mining and Knowledge Discovery, vol. 5, no. 5, pp. 216–233, 2015.
D. Kocev, S. Džeroski, M. D. White, G. R. Newell, and P. Griffioen, “Using single- and multi-target regression trees and ensembles to model a compound index of vegetation condition,” Ecological Modelling, vol. 220, no. 8, pp. 1159–1168, Apr. 2009.
D. Tuia, J. Verrelst, L. Alonso, F. Perez-Cruz, and G. Camps-Valls, “Multioutput support vector regression for remote sensing biophysical parameter estimation,” IEEE Geoscience and Remote Sensing Letters, vol. 8, no. 4, pp. 804–808, Jul. 2011.