Multi-agent AI systems for detecting emerging therapeutic targets and intervention patterns in neuroplasticity research

The exponential growth of neuroplasticity research presents profound challenges to the manual synthesis of literature, hindering the identification of emerging therapeutic targets and intervention patterns. While multi-agent Artificial Intelligence (AI) systems have been proposed conceptually to address this, concrete implementations demonstrating end-to-end utility are scarce. This paper details the successful implementation and application of a novel four-agent AI system designed to automate the discovery, extraction, analysis, and validation of patterns in neuroplasticity literature. Our platform consists of four parts: a Literature Discovery Agent (LDA) for corpus gathering, a Concept Extraction Agent (CEA) with a multi-level NLP strategy fallback mechanism for improving resilience, a Pattern Analysis Agent (PAA) utilizing machine learning for thematic grouping and trend analysis, and a Validation Agent (VA) purely for the validation phase. The authors have a specific case study on neuroplasticity with respect to stroke rehabilitation research, where the platform automatically processed 533 scientific papers, extracted 4,393 biomedical entities, and isolated four research sub-fields that are not only statistically significant but also relevant to the topic: (1) Vagus Nerve Stimulation, (2) Molecular and Synaptic Processes involving Brain-Derived Neurotrophic Factor (BDNF), Clinical and Music Therapies, and finally (3) Brain-Computer Interface and Motor Training. This research project illustrates the efficacy and utilization capacity of collective AI validation beyond any purely conceptual framework.