AI-Driven Cognitive Assessment Using Natural Language Patterns for Early Detection of Neuropsychological Disorders
Keywords:
cognitive assessment, natural language processing, neuropsychological disorders, early detection, system architecture, fairness, governance, health informaticsAbstract
The early detection of neuropsychological disorders such as mild cognitive impairment, Alzheimer’s disease, and major depressive disorder remains a critical public health challenge, often complicated by subjective clinical assessments and late-stage biomarkers. Recent advances in natural language processing and machine learning have opened new possibilities for unobtrusive, scalable cognitive assessment through the analysis of spontaneous language productions. This paper presents a system-level investigation into the design, deployment, and governance of AI-driven platforms that infer cognitive status from natural language patterns. We examine the architectural trade-offs between cloud-based and edge-native processing paradigms, the structuring of multimodal data ingestion pipelines, and the challenges of ensuring fairness across diverse sociolinguistic populations. A central theme is the tension between model interpretability and predictive performance, particularly when models are integrated into clinical decision-support workflows. We discuss infrastructure requirements for longitudinal monitoring, including secure data federation, model versioning, and compliance with evolving health data regulations. The sustainability of such systems is analyzed from both computational and organizational perspectives, emphasizing the need for robust evaluation frameworks that extend beyond laboratory benchmarks. Further, we address the policy implications of automated cognitive screening, including the risk of overdiagnosis, algorithmic stigmatization, and the ethical handling of incidental findings. Throughout, we argue that system designers must balance the promise of early, scalable detection with the imperatives of equitable access, transparent reporting, and meaningful human oversight, ultimately advocating for a socio-technical architecture that embeds clinical validation and community engagement from the earliest stages of development.
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