Cite this article
Zhou Hongyu, Li Zixiao, Wang Chunjuan. Full-course closed-loop management for neurological disorders through artificial intelligence-powered terminal devices[J]. Chinese Journal of Stroke, 2025, 20(8): 935-941.
With the intensification of population aging, the prevalence of neurological disorders such as stroke, dementia, Parkinson’s disease, and epilepsy continues to rise, posing a significant challenge to public health in our country. Neurological disorders are characterized by chronicity and symptom fluctuations, and their effective management relies on long-term, dynamic, and precise monitoring and individualized interventions. However, the current hospital-centered treatment model mainly relies on intermittent assessments during outpatient visits or hospitalizations, which has limitations such as low monitoring frequency, fragmented data, and strong subjectivity. Especially in the context of uneven distribution of medical resources and weak home management capabilities among patients, patients often find themselves in a “management vacuum” after discharge, making it difficult to timely identify changes in their condition outside the hospital. Insufficient monitoring of conditions outside the hospital can lead to delayed warnings and interventions, severely affecting patients’ prognosis and quality of life.
In recent years, the rapid development of artificial intelligence technology, along with the in-depth application of key technologies such as deep learning, natural language processing, and computer vision in the medical field, has formed a synergistic effect with the promotion of smartphones, wearable devices, and home smart terminals, providing a solid foundation for constructing a continuous and intelligent health management system covering “hospital-community-home.” This article aims to systematically elaborate on the application of the integration of artificial intelligence and terminal devices in the full-course management of neurological disorders, promoting the transformation of the treatment model from passive response to active monitoring, intelligent early warning, and dynamic feedback closed-loop management, providing theoretical basis and practical pathways for establishing a new paradigm of adaptive, precise, and continuous health management.
Part.1
Artificial Intelligence Empowered Terminal Devices and Their Applications
1.1 Smartphones
Smartphones, with their multimodal sensors and artificial intelligence algorithms, have become an important carrier for achieving multidimensional remote assessment of neurological disorders. For example, by analyzing voice features (such as speech disorders and slowed speech rate), they provide objective evidence for the early identification of neurological disorders such as stroke; using accelerometers and gyroscopes to collect motion data can quantify and assess gait abnormalities and motor slowness, achieving dynamic monitoring of the condition; embedded cognitive assessment applications can conveniently detect patients’ memory, executive function, and other cognitive domain performances, enhancing the accessibility of screening and patient compliance. As a data integration hub, smartphones can merge information from wearable devices and environmental sensors, enabling real-time symptom data upload, personalized feedback, and doctor-patient interaction through dedicated applications, enhancing the continuity of follow-up and patient engagement in disease management. Additionally, smartphones can also integrate large language models to provide health consultations, medication reminders, and risk warning services, further enhancing patients’ self-management capabilities.
1.2 Wearable Devices
Wearable devices (such as smartwatches, smart rings, and patch sensors) are portable and capable of continuous monitoring, showing broad application prospects in the management of neurological disorders. Integrated multimodal sensors can real-time collect physiological parameters such as heart rate, body temperature, blood oxygen saturation, blood pressure, and blood glucose, dynamically reflecting the state of the autonomic nervous system. High-precision inertial sensors and surface electromyography technology can accurately quantify motion parameters, achieving long-term unobtrusive monitoring of patients with comorbid motor disorders (such as stroke and Parkinson’s disease), thus overcoming the limitations of traditional disease monitoring methods that can only assess intermittently. AI-powered wearable devices can monitor clinical events such as cardiac arrest, atrial fibrillation, falls, Parkinson’s disease motor “off periods,” or seizures in real-time and automatically trigger alarms, significantly enhancing the safety of patients with neurological disorders at home.
1.3 Environmental Intelligence
Environmental intelligence relies on a network of smart home sensors (such as cameras, microphones, and wireless signal sensing devices) and employs non-contact monitoring technology to continuously collect and analyze the daily activity patterns, sleep structures, and behavioral characteristics of patients with neurological disorders. This technology can sensitively capture changes such as reduced activity, nighttime wandering, and abnormal breathing rhythms, and, combined with AI models, identify their association with cognitive decline or deterioration of motor function, achieving early risk warnings. Compared to wearable devices, environmental intelligence avoids compliance issues associated with wearing devices, significantly enhancing the feasibility and comfort of long-term monitoring. Importantly, environmental intelligence can achieve “invisible monitoring” at home while ensuring patient privacy and autonomy, providing objective and continuous data support for clinical interventions and optimizing long-term management strategies for neurological disorders.
1.4 Robots
Robotic technology has shown significant roles in the rehabilitation and long-term management of neurological disorders. Exoskeletons and rehabilitation robots integrate brain-computer interfaces, wearable devices, and AI algorithms to develop personalized training programs based on the individual motor function status of patients, and dynamically adjust the intensity and mode of interventions through real-time biofeedback, thus achieving adaptive rehabilitation training for patients, promoting their neural plasticity, and improving treatment compliance. Service robots also have considerable application potential in cognitive training, medication reminders, daily living assistance, and social interaction support. Continuously monitoring changes in patients’ behavioral patterns helps to identify early signs of cognitive decline, emotional abnormalities, or social isolation risks, enabling proactive interventions. Robotic technology not only enhances patients’ self-management capabilities and quality of life, alleviates the caregiving burden on family members, but also compensates for the lack of community and home rehabilitation resources, promoting the intelligent and individualized development of neurological disorder rehabilitation.
Part.2
Core Elements for Building Closed-Loop Management
2.1 Collection and Integration of Full-Process Data
The primary step in achieving closed-loop management of neurological disorders is the multidimensional and continuous collection and integration of data. Smartphones, wearable devices, environmental intelligence, and robots can simultaneously collect multimodal data from patients, including physiological parameters (such as heart rate variability), motion characteristics (such as gait and tremors), behavioral patterns, environmental information, and patient-reported outcomes. These data are characterized by high frequency, heterogeneity, and temporal variability, making effective integration through traditional methods challenging. AI technology can significantly reduce data noise and bias through data cleaning, time alignment, feature extraction, and multi-source information fusion, constructing high-fidelity, personalized datasets. AI technology can also establish a “digital twin” model representing individual disease states based on the dynamic evolution patterns of the data, providing a high-quality, continuous data foundation for intelligent analysis and precise intervention of diseases.
2.2 Algorithm-Driven Intelligent Analysis and Decision Support
Based on multimodal data fusion, AI algorithms form the core engine of intelligent management. Machine learning and deep learning algorithms can objectively quantify the severity of diseases, clinical classifications, and treatment responses. Based on longitudinal monitoring data, AI can construct risk prediction models for neurological disorders, identifying high-risk events such as stroke recurrence, seizures, or cognitive decline in advance, achieving early warnings oriented towards “preventive treatment.” Furthermore, AI systems can simulate multiple intervention pathways to generate personalized treatment recommendations for diseases, covering medication adjustments, rehabilitation plans, and non-drug intervention strategies, thereby enhancing the scientific and adaptive nature of clinical decision-making. Such data-driven decision support systems provide a scalable new paradigm for the dynamic management of complex diseases.
2.3 Adaptive Closed-Loop Feedback and Intervention
The key to closed-loop health management lies in achieving a dynamic cycle of “monitoring-analysis-decision-intervention-feedback.” AI systems can push personalized feedback to patients or caregivers in real-time, such as providing exercise guidance, cognitive training tasks, or lifestyle suggestions, thereby enhancing patients’ long-term self-management capabilities. Adaptive closed-loop feedback systems can further participate in deep management of diseases, automatically triggering intervention measures, such as adjusting insulin doses, optimizing rehabilitation training plans, or regulating deep brain stimulation parameters. Importantly, this closed-loop system possesses continuous learning capabilities, such as dynamically collecting physiological and behavioral response data post-intervention to evaluate efficacy and iteratively updating prediction models and decision algorithms, achieving adaptive updates of management strategies. This “learning-optimizing-reintervening” closed-loop mechanism promotes the intelligent transition of neurological disorder management from static guidance to dynamic evolution, significantly enhancing its long-term management effectiveness.
2.4 Integrated Model of “Hospital-Community-Home”
To achieve continuous health management of neurological disorders, it is essential to eliminate information barriers between medical institutions and home settings. With standardized data interfaces and secure transmission protocols, interconnectivity between in-hospital diagnostic data and out-of-hospital long-term monitoring information can be achieved, constructing a complete view of the patient’s disease course. Meanwhile, AI management platforms can provide community and family doctors with a visual overview of patient status and graded warning prompts, enhancing the ability of primary healthcare institutions to recognize and initially manage neurological disorders. Additionally, AI-powered terminal devices support remote expert consultations and intervention guidance, facilitating the downward flow of quality medical resources to grassroots levels. This collaborative model not only alleviates the disease follow-up pressure on large hospitals but also enables patients to receive timely and professional management support in communities and homes, promoting the formation of a new ecosystem of integrated and collaborative health services for neurological disorders, effectively improving the accessibility of medical services and overall management quality.
Part.3
Challenges
While AI-powered terminal devices hold great potential in the management of neurological disorders, their clinical translation still faces multiple challenges. ① The quality and standardization of multi-source data are prominent issues. The heterogeneity in sensor accuracy, data noise, and formats across different devices significantly affects the reliability and comparability of analysis results. ② AI models often exhibit “black box” characteristics, lacking interpretability, and their robustness and generalization capabilities in different patient populations, disease stages, and usage environments require systematic validation. ③ Privacy protection and data security are key bottlenecks in the application of AI-powered terminal devices. The collection, storage, and sharing of vast amounts of sensitive health information must strictly adhere to ethical norms and legal regulations, implementing security management throughout the entire data lifecycle. ④ Large-scale, multi-center, prospective clinical studies are still needed to verify the clinical effectiveness, cost-effectiveness, and long-term safety of AI-powered terminal devices, thereby meeting regulatory requirements. ⑤ The issue of the digital divide cannot be ignored. Differences in service accessibility for elderly, low-income, and technology-limited populations may exacerbate healthcare inequalities. ⑥ AI-assisted decision-making must clarify clinical boundaries. The dominant role of clinical physicians in disease closed-loop management should be strengthened to avoid over-reliance on technology, ensuring the scientific nature of medical decisions and the continuation of humanistic care.
Part.4
Outlook: Intelligent Integration and Ecological Collaboration
AI-driven health management for neurological disorders is moving towards a new stage of deep intelligent integration, system integration, and multi-entity collaboration. Multimodal perception technologies will integrate data from smartphones, wearable devices, environmental intelligence, robots, and applications, achieving comprehensive analysis of patients’ physiological, behavioral, and environmental factors, thereby enhancing the representation accuracy and predictive effectiveness of the “digital twin” model. Technologies such as federated learning and edge computing can support cross-institutional collaborative modeling while ensuring data privacy, reducing transmission delays, and enhancing the system’s real-time responsiveness. Generative AI technology is expected to be applied in automatically generating structured clinical reports and constructing personalized virtual health assistants to assist clinical decision-making and patient management. More importantly, AI and terminal devices will be deeply integrated into digital therapeutics, evolving into verifiable and regulated prescription-level intervention tools, gradually merging into standardized treatment pathways. In the future, it is necessary to build a collaborative ecosystem involving medical institutions, technology companies, insurance payers, regulatory agencies, and community service systems, promoting the transformation of neurological disorder management towards intelligent, standardized, and sustainable directions. There is an urgent need to strengthen multi-party collaboration among medicine, engineering, industry, policy, and patient groups, accelerate technological iteration and evidence accumulation, and improve ethical norms and policy frameworks. Looking forward to the era of “smart neurological health,” patient-centered AI will achieve seamless linkage with terminal devices, providing services such as proactive monitoring, intelligent early warnings, and dynamic feedback, offering strong support for constructing a new paradigm of full-course closed-loop management of neurological disorders.
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Written by: Zhou Hongyu, Li Zixiao, Wang Chunjuan
Edited by: Peng Hanyu
Reviewed by: Cai Rong