NeuroVLM-Bench: Evaluation of Vision-Enabled Large Language Models for Clinical Reasoning in Neurological Disorders

1. 1Curate neuroimaging datasets for multiple neurological conditions
2. 2Evaluate vision-enabled large language models using the curated datasets
3. 3Assess reliability and operational trade-offs of the models
4. 4Compare performance of different models for clinical reasoning tasks

AI engineers and researchers in the healthcare industry can benefit from this study to develop more accurate and reliable image-based decision support systems, while clinicians can use the findings to improve diagnosis and treatment of neurological disorders

💡 Vision-enabled large language models have potential for image-based decision support in neurological disorders, but their reliability and operational trade-offs need to be carefully evaluated

🧠💡 Evaluating vision-enabled LLMs for neuroimaging-based clinical reasoning #AIinHealthcare