Leveraging multimodal large language models to extract mechanistic insights from biomedical visuals: A case study on COVID-19 and neurodegenerative diseases

The COVID-19 pandemic has intensified concerns about its long-term neurological impact, with evidence linking SARS-CoV-2 infection to neurodegenerative diseases (NDDs) such as Alzheimer’s (AD) and Parkinson’s (PD). Patients with these conditions face higher risk of severe COVID-19 outcomes and may experience accelerated cognitive or motor decline following infection. Proposed mechanisms—including neuroinflammation, blood–brain barrier (BBB) disruption, and abnormal protein aggregation—closely mirror core features of neurodegenerative pathology. However, current knowledge remains fragmented across text, figures, and pathway diagrams, limiting integration into computational models that could reveal systemic patterns. To address this gap, we applied GPT-4 Omni (GPT-4o), a multimodal large language model (LLM), to extract mechanistic insights from biomedical figures. Over 10,000 images were retrieved through targeted searches on COVID-19 and neurodegeneration; after automated and manual filtering, a curated subset was analyzed. GPT-4o extracted biological relationships as semantic triples, grouped into six mechanistic categories—including microglial activation and barrier disruption—using ontology-guided similarity and assembled into a Neo4j knowledge graph (KG). Accuracy was evaluated against a gold-standard dataset of expert-annotated images using Biomedical Bidirectional Encoder Representations from Transformers (BioBERT)–based semantic matching. This evaluation enabled prompt tuning, threshold optimization, and hyperparameter assessment. Results show that GPT-4o successfully recovers both established and novel mechanisms, yielding interpretable outputs that illuminate complex biological links between SARS-CoV-2 and neurodegeneration. This study demonstrates the potential of multimodal LLMs to mine biomedical visual data at scale. By complementing text mining and integrating figure-derived knowledge, our framework advances understanding of COVID-19–related neurodegeneration and supports future translational research.