Deep Learning in Medical Robotics for Parkinson's disease Symptom Assessment

Abstract

This study investigates the manner in which to diagnose and treat Parkinson's disease using deep learning-based medical robotics. The study analyses the possibilities of these technologies adopting an interpretivist philosophy, a deductive strategy, as well as a descriptive design. The findings show that deep learning models may greatly increase the accuracy of symptom analysis, particularly their incorporation with medical robots improves assessment effectiveness. Clinical analyses show that these systems perform better than traditional approaches, which opens the door to earlier interventions and more specialized care. However, the study emphasizes the necessity of standardization, moral issues, as well as a widening of applications. To ensure the viability and efficacy of these cutting-edge technologies, future research should concentrate on long-term monitoring and its impact on treatment options.