An Explainable Clinical NLP Chatbot for Alzheimer's Disease Screening and Treatment Guidance

Abstract

Alzheimer's Disease (AD) represents one of the most challenging neurodegenerative disorders affecting millions worldwide, yet early detection remains limited. This paper introduces a hybrid chatbot system that combines adaptive cognitive assessments with clinical Natural Language Processing (NLP) and explainable artificial intelligence to perform comprehensive AD screening and provide personalized treatment recommendations. Bio ClinicalBERT enables advanced clinical text interpretation with demonstrated superiority on medical corpora, while LIME (Local Interpretable Model-Agnostic Explanations) ensures transparent decision-making, and AES-256 encryption supports secure data handling. The proposed framework integrates transformer-based NLP models with rule-based clinical reasoning through an API-driven architecture to deliver customized screening assessments and evidence-based therapeutic suggestions. Experimental evaluation demonstrates strong performance, achieving 94.2% accuracy in cognitive assessment classification and 89.7% precision in treatment recommendation alignment with established clinical guidelines. The explainability component enhances trust among healthcare providers through interpretable decision pathways, while integrated appointment scheduling and secure data management improve practical usability. Validated primarily on Western clinical populations, this comprehensive system integrates multiple AD-related healthcare functions while maintaining clinical interpretability and regulatory compliance. The approach advances early AD detection and management by offering a cost-effective, accessible, and clinically validated solution suitable for resource-constrained environments.

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