Artificial Intelligence in Remote Monitoring and Telemedicine

  • Abbasi N
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Abstract

Telemedicine, remote monitoring coupled with Artificial Intelligence innovations are redesigning the face of the healthcare sector in record way and increasing satisfaction levels for patients’ clinical enhancements, reduced charges and increased effectiveness in the delivery of services. The applied and advanced AI technologies consist of machine learning, natural language processing, and predictive analytics used in applications for RPM, PT, and VC. In RPM, AI augments the processes of data aggregation and data analysis of patients’ real-time health data from wearable and other digital health technologies. The former capability makes it possible to check for the possible infections, caretaker interferences, and regular management of recurring diseases. For example, AI can help in anticipating incidents such as heart attacks based on previous data of patients, thus preventive care is implemented. On the other hand, AI is incorporated in telemedicine through applying virtual health assistance, diagnostic tools and even chats. With the help of AI, virtual assistants can filter patients’ complaints, give first-stages diagnoses, and suggest necessary treatments, which will decrease loads of clinicians and increase availability of medical services. This review analyses the current uses, advantages and disadvantages of AI in remote observation and m-telemedicine. Here the details what kind of AI technologies implied in different spheres of healthcare, diagnosing, treatment planning, and individual therapies. Also, it examines how the responsible AI should be implemented ethically in a healthcare setting. Moreover, it presents the further prospects of AI in telemedicine which underlines the importance of AI development and enhancement of patient treatment.

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APA

Abbasi, N. (2024). Artificial Intelligence in Remote Monitoring and Telemedicine. Journal of Artificial Intelligence General Science (JAIGS) ISSN:3006-4023, 1(1), 258–272. https://doi.org/10.60087/jaigs.v1i1.202

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