AI-Powered Remote Diagnosis and Personalized Treatment Plans in Telemedicine Systems

Abstract

Telemedicine systems have revolutionized healthcare by overcoming geographical challenges and facilitating remote access to medical treatments. This paper examines the use of Artificial Neural Networks (ANNs) in telemedicine systems, highlighting their function in automating diagnoses and enhancing treatment recommendations. ANNs have enhanced these systems, allowing precise remote diagnosis and personalized treatment programs. After the human brain, the ANN model is proficient at analyzing complex and multidimensional medical data, such as electronic health records (EHRs), diagnostic imaging, and real-time physiological signals. By uncovering concealed patterns and connections, ANN improves diagnostic accuracy and facilitates the creation of personalized therapy approaches based on unique patient characteristics. It illustrates how ANN-driven systems evaluate patient data to forecast disease development and suggest treatments. Although these breakthroughs enhance healthcare results, issues such data privacy, ethical implications, and computational demands are substantial and need comprehensive solutions. The results highlight the potential of ANN-driven telemedicine systems to transform patient care by providing precise, efficient, and personalized medical services, facilitating wider acceptance in international healthcare.

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