Intelligent Neurohealth Systems
Abstract
Artificial intelligence neuroscience (AI) in combination with neural sciences gives rise to the Intelligent Neurohealth Systems, or rather INHS, that disrupt both diagnostics and treatment therapies. Such systems apply techniques of machine learning, deep learning, and neuroinformatics to boost the accuracy of ascertaining neurological disorders, planning treatments, and monitoring patients. By analyzing massive datasets from neuroimages, genomics, and electronic health records, INHS provides unprecedented insights into the detection and progression of neurological diseases at productivity peak levels. Furthermore, AI technology, through personalized pharmacogenomics, optimizing drug regimens tailored to individuals’ genetic profiles, along with adaptive deep brain stimulation, has shown tremendous capability to improve therapeutic results. This paper discusses the major technologies enabling INHS, which include machine learning–based algorithms, brain-computer interfaces, and robotic rehabilitation. It focuses on their uses in neurology through AI-aided diagnostic tools for Alzheimer's and Parkinson's diseases, pacemaker systems for seizure prediction, and remote caregiving telemedicine systems for uninterrupted patient monitoring. Also, we analyze the limitations of implementing such systems, like the privacy of complex data, bias of AI models, and lack of governance policies. With studies and more AI case studies showcasing advanced technologies, we demonstrate the new solutions engineered with AI for neurohealth. We consider directions such as the implementation of quantum computing for quicker analysis of neuromata, AI-powered mental health applications, and the creation of explainable AI models to be used in clinical settings to enhance trust in AI outputs. Intelligent Neurohealth Systems, incorporated with modern technologies, have the potential to revolutionize the diagnosis, management, and treatment of neurological disorders, paving the way towards enhanced, customized healthcare.
Publication details
- Pages
- 741–761
- Publication date
- 9 January 2026
- Language
- EN