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Research Paper | Information Technology | India | Volume 12 Issue 12, December 2023 | Popularity: 2.9 / 10

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Telemedicine Transformation: A Deep Learning Approach to Virtual Patient Diagnostics

Ramanakar Reddy Danda

Abstract: Telemedicine has seen great advances and is now able to provide virtual care to patients. In particular, doctors and nurses have already used telemedicine platforms for almost 20 years to guide unschooled home caregivers during the hospitalization of patient relatives. Today, with the support of hospitalized patients who use gamified human - machine interfaces, the same homebound relatives remain in touch with the patient enjoyably. For patient carers to complete their tasks as painlessly as possible, our proposed technology enhances the clinical spectrum of telemedicine with artificial intelligence. We have adapted an end - to - end deep learning architecture to recognize up to 16 physical reactions associated with 13 physical activities so far that can be performed by virtual patients. Most results shown in the text have been obtained with the support of parallel chips along with a CPU on a local Multi Adversarial Learning System. Deep learning greatly improves telemedicine, enhances virtual patient diagnostics, extends the clinical spectrum of telemedicine to patient - related home caregivers, and helps interfaces to correctly recognize inpatient wishes. The adaptive multi - adversarial end - to - end architecture deep learner architecture can identify up to 16 physical reactions that go along with up to 13 stressor activities that were performed by a virtual patient. Integrating with telemedicine is beneficial in the short to medium term and the long run. A broad spectrum of scientists, industries, and institutions agrees with this assessment. However, only a minority of these entities have already developed solutions that include telemedicine services and voice interfaces. Evaluating the advantages and disadvantages of this mixture can help to clarify the confused and contradictory situation in which telemedicine is operating.

Keywords: Telemedicine, Virtual Care, Artificial Intelligence, Deep Learning, Human - Machine Interfaces, Home Caregivers, Virtual Patients, Physical Reactions, Physical Activities, Gamification, Multi - Adversarial Learning, End - to - End Architecture, Stressor Activities, Parallel Chips, CPU Integration, Patient Diagnostics, Voice Interfaces, Clinical Spectrum, Adaptive Technology, Telemedicine Platforms

Edition: Volume 12 Issue 12, December 2023

Pages: 2191 - 2199

DOI: https://www.doi.org/10.21275/SR231214144932

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