SIMULATION OF AI-DRIVEN COGNITIVE ASSESSMENT AND PERSONALIZED TASK RECOMMENDATION FOR ENHANCING COGNITIVE FUNCTION IN PARKINSON’S DISEASE PATIENTS

Abstract

Aim Parkinson’s disaese is a disorder that leads to large scale of cognitive impairments like reasoning, planning, memory and execution, often reducing independence. This research explored how Artificial Intelligence (AI) could serve as a virtual but clinical tool to detect these cognitive struggles and suggest personalized exercises to improve them. We created a rule-based AI solution that diagnoses cognitive test performance and generates personalized rehabilitation plans focusing on transparency and strict adherence to clinical standards. Results Performance of the PD patients' cognitive test was measured for working memory, attention, visuospatial abilities, and planning. The AI system provided explicit recommendations for tailored exercises and activities, e.g., attention training in the event of attention impairments or spatial training in the presence of visuospatial impairments, depending on individual scores. The recommendations aligned very well with clinical expert recommendations, indicating reliability and a capacity to identify subtle patterns to offer tailor-made training programs. Conclusion This research shows the ability of AI to improve cognitive care for Parkinson's disease. A basic rule-based strategy offered personalized suggestions that might prove beneficial for PD patients to optimize cognitive capacity. Through the intersection of testing and personalized training, AI may liberate clinicians from their workload and provide quick and scalable results and solutions too. This strategy enables patient- specific personalized proactive care for cognitive impairment in PD to promote improvement in patients' lives.