Advancing Telestroke Services Technology with Cloud and AI Solutions for Enhanced Care
Telestroke services have become an essential component of stroke therapy, especially in areas with few specialists' medical facilities. To further improve telestroke services, this paper investigates the possibility of integrating cloud computing with artificial intelligence (AI) technologies. With the scalability, accessibility, and cost-effectiveness of cloud infrastructure, healthcare professionals worldwide may communicate and share data easily. Machine learning algorithms, image analysis tools, and other forms of AI are enhancing telestroke services by giving doctors access to sophisticated decision-making capabilities. Better patient outcomes result from these systems' capacity to facilitate quicker and more precise diagnosis and treatment planning. Predictive analytics powered by AI can identify patients at risk early on and provide individualized treatment plans that can better meet each patient's requirements. By combining cloud and AI technology, telestroke services can improve patient care by analyzing large datasets, allocating resources more efficiently, and fostering better teamwork among specialists. It may perform beyond geographical restrictions and ensure that stroke patients all around the globe have equal access to high-quality treatment. The potential is to transform stroke care delivery using data-driven insights and improved decision-making skills. It simplifies processes and improves efficiency.
Authors
- P.K. Dhal (ORCID: https://orcid.org/0000-0002-9449-6099)
- M. Pallikonda Rajasekaran
- R. Meenakshi
- R. Aiyshwariya Devi
- S. Sujatha (ORCID: https://orcid.org/0000-0003-3456-1220)
- R. Kamalakannan (ORCID: https://orcid.org/0000-0002-3980-1505)
Institutions
- Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (IN)
- Saveetha University (IN)
Publication Details
- Published
- 2024-11-06
- DOI
- https://doi.org/10.1109/icstsdg61998.2024.11026671
- Primary Topic
- Impact of AI and Big Data on Business and Society
- Type
- article
- Field-Weighted Citation Impact
- 0.00