Enhancing Patient Care with AI-Driven Remote Monitoring and Predictive Alerts

Kumar Avizeet

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2025, vol. 11, issue 1, 3155-3169

Abstract: Due to advancements in Artificial Intelligence (AI), its use in developing disease detection algorithms has surged. AI offers major benefits across various sectors like automotive, finance, IT, and pharmaceuticals. It is categorized into strong AI, which operates independently of human input, and weak AI, reliant on rules to make informed decisions. This essay focuses on weak AI, which enhances decision-making probabilities and finds application in healthcare. Healthcare is critical for individuals needing immediate medical attention. It interacts with other fields, including pharmaceuticals and telecommunications, revolutionized by advances in technology and ICT integration, making healthcare systems more efficient and cost-effective. A notable trend in healthcare is the adoption of AI-enabled Remote Patient Monitoring (RPM), which provides predictive alerts. AI has improved disease detection systems, enhancing their accuracy and efficiency. With AI, health vitals and trends can be predicted, as it monitors all patient events and initiates necessary actions. Traditional healthcare measures vitals periodically, whereas AI-enabled RPM allows for prior health predictions, facilitating timely reactions in emergencies.

Keywords: Remote Patient Monitoring (RPM); Predictive Analytics; Healthcare Data Analytics; Artificial Intelligence (AI); Patient Monitoring Systems; Predictive Alerts; Health Informatics; Telehealth; Machine Learning; Patient Care Management; Wearable Devices; Healthcare IoT; Chronic Disease Management; Real-Time Monitoring; Personalized Medicine (search for similar items in EconPapers)
Date: 2025
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT2511128
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijsrcseit.com/home/article/view/CSEIT2511128 Article URL (text/html)
https://ijsrcseit.com/home/article/download/CSEIT2511128/CSEIT2511128 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v11:y2025:i1:id:992

DOI: 10.32628/CSEIT2511128

Access Statistics for this article

More articles in International Journal of Scientific Research in Computer Science, Engineering and Information Technology from International Journal of Scientific Research in Computer Science, Engineering and Information Technology
Bibliographic data for series maintained by Pankaj Sharma (USA) ().