[2603.24846] NeuroVLM-Bench: Evaluation of Vision-Enabled Large Language Models for Clinical Reasoning in Neurological Disorders

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.24846 (cs) [Submitted on 25 Mar 2026] Title:NeuroVLM-Bench: Evaluation of Vision-Enabled Large Language Models for Clinical Reasoning in Neurological Disorders Authors:Katarina Trojachanec Dineva, Stefan Andonov, Ilinka Ivanoska, Ivan Kitanovski, Sasho Gramatikov, Tamara Kostova, Monika Simjanoska Misheva, Kostadin Mishev View a PDF of the paper titled NeuroVLM-Bench: Evaluation of Vision-Enabled Large Language Models for Clinical Reasoning in Neurological Disorders, by Katarina Trojachanec Dineva and 7 other authors View PDF HTML (experimental) Abstract:Recent advances in multimodal large language models enable new possibilities for image-based decision support. However, their reliability and operational trade-offs in neuroimaging remain insufficiently understood. We present a comprehensive benchmarking study of vision-enabled large language models for 2D neuroimaging using curated MRI and CT datasets covering multiple sclerosis, stroke, brain tumors, other abnormalities, and normal controls. Models are required to generate multiple outputs simultaneously, including diagnosis, diagnosis subtype, imaging modality, specialized sequence, and anatomical plane. Performance is evaluated across four directions: discriminative classification with abstention, calibration, structured-output validity, and computational efficiency. A multi-phase framework ensures fair comparison while controlling for selection bias...