Deep Learning based Frailty Detection in Conjuction with Telemedicine

Abstract

Telemedicine is a service provided by healthcare providers to patients seeking medical attention remotely. This paper explores the role of Artificial Intelligence (AI) and Deep Learning in enhancing the quality of telemedicine services. AI which includes deep learning, data science, and machine learning, enables the development of smart applications that can assist both patients and medical professionals in a more efficient way. Deep Learning is a subset of machine learning. This paper explores the related works, and smart AI models to help physicians to improve telemedicine services. This paper explores the implementation of AI in healthcare services. In this research paper, we propose the implementation of our Heart Attack Risk Predictive model to overcome the challenges in telemedicine services, achieving 98% accuracy. The traditional healthcare process involves multiple steps, scheduling an appointment with the cardiologist undergoing prescribed tests, collecting the test results, and finally receiving a recommendation for the treatment. A theoretical approach of deploying a model within a web portal to eliminate the frictions is discussed in this paper which has the potential to revolutionize remote medical treatment.

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