Advanced Robotics in Healthcare Using Hybrid Visual Geometry Residual Networks for Enhanced Diagnostics

In the health sector, robotics and Artificial Intelligence (AI) have greatly influenced the direction of patient care, diagnosis, and treatment outcomes, especially for elderly patients. With a view to ensuring real-time monitoring and quick medical response, the demand for sophisticated healthcare monitoring systems is rising with an aging population. Conventional medical monitoring methods often endure inefficiencies, including misclassifiability, vulnerability to noise, and processing slowdown, causing false diagnoses or lag in reacting to life-threatening disease. This model, a hybrid Visual Geometry Residual Network (VGRN), relies on VGG-16 strengths and the ability of ResNet-50 to counter such disadvantages and elevate classification accuracy in cases of illness. To enhance signal clarity and uniform feature representation, the system utilizes sophisticated data preprocessing tools like Fast Fourier Transform (FFT) for efficient feature extraction and Gaussian filtering to suppress noise. A robotic aid system is employed to offer emergency response, medication adherence monitoring, and cognitive support, allowing real-time monitoring and immediate action. Improved patient outcomes are the result of the greater predictive analytics and disease detection of an AI-driven solution. With its remarkable accuracy of 98.52% and precision rate of 98.24%, the test result confirms the efficiency of the proposed system and far exceeds traditional approaches to health monitoring. The technology, which integrates intelligent robotic care and AI diagnostics, not only enhances the effectiveness of treatment but also reduces caregiver workloads, making elder care more convenient and efficient. By using sophisticated technology, the proposed model has an important role to play in enabling proactive management of healthcare, minimizing emergency situations, and ultimately enhancing the elderly’s quality of life.