Title : AI-assisted collective analysis of neurological biomarkers: A study for enhanced diagnostic accuracy in neurodegenerative disorders
Abstract:
Neuromorphic Collective Intelligence System for Enhanced Neurological Diagnostic Accuracy.
Background: Traditional neurological diagnosis relies heavily on individual clinician expertise, leading to significant variability in diagnostic accuracy across institutions and specialties. The complexity of neurological disorders, particularly neurodegenerative diseases, often requires integration of diverse clinical perspectives and computational analysis of multimodal biomarker data that exceeds individual cognitive processing capabilities.
Objective: To develop and validate a Neuromorphic Collective Intelligence System (NCIS) that integrates human neurological expertise with artificial intelligence through advanced brain-computer interfaces for improved diagnostic accuracy in complex neurological disorders.
Approach: The NCIS framework combines non-invasive neural interfacing using high-density EEG arrays with federated learning algorithms that preserve individual cognitive boundaries while enabling collective diagnostic reasoning. The system employs a linguistic-neural translation layer that maps neural activity patterns from participating neurologists to semantic representations in the artificial system's knowledge base. This enables seamless integration of human intuitive pattern recognition with computational analysis of neuroimaging, electrophysiology, and biomarker data.
The architecture maintains three operational layers: local cognitive models that adapt to individual neurologist expertise patterns, task-specific integration models for different diagnostic challenges, and a global coordination layer managing information flow while preventing cognitive overload. Fairness-verified algorithmic governance ensures equitable weighting of diverse specialist perspectives based on subspecialty expertise and case complexity.
Anticipated Outcomes: NCIS is expected to significantly improve diagnostic accuracy for challenging neurological cases, particularly in differential diagnosis of neurodegenerative disorders and early-stage disease detection. The system should reduce diagnostic variability between institutions and provide enhanced diagnostic support for community hospitals with limited subspecialty access.
