Utilizing Artificial Intelligence in Telemedicine for Efficient Remote Diagnosis and Treatment Plan

Abstract

To facilitate dynamic and adaptive remote diagnosis and treatment planning, the “Dynamic Adaptive Diagnosis and Treatment (DADT)” approach uses three fundamental AI algorithms that operate in tandem with one another. The cornerstone is the Intelligent Symptom Analysis Algorithm (ISAA), which uses patient-reported symptoms and medical history to make diagnostic determinations. The Adaptive Treatment Recommender (ATR) algorithm constantly adjusts prescribed treatments considering patient feedback, research findings, and new standards of care. In order to constantly learn from fresh data and update diagnostic and treatment models, the Continuous Learning Diagnostic Network (CLDN) makes use of a neural network. Accurate diagnosis and individualized treatment planning are the goals of the suggested technique, which is intended for use in distant healthcare settings.

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