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🧠 AI⚪ NeutralImportance 6/10
NeuroVLM-Bench: Evaluation of Vision-Enabled Large Language Models for Clinical Reasoning in Neurological Disorders
arXiv – CS AI|Katarina Trojachanec Dineva, Stefan Andonov, Ilinka Ivanoska, Ivan Kitanovski, Sasho Gramatikov, Tamara Kostova, Monika Simjanoska Misheva, Kostadin Mishev|
🤖AI Summary
Researchers benchmarked 20 multimodal AI models on neuroimaging tasks using MRI and CT scans, finding that while technical attributes like imaging modality are nearly solved, diagnostic reasoning remains challenging. Gemini-2.5-Pro and GPT-5-Chat showed strongest diagnostic performance, while open-source MedGemma-1.5-4B demonstrated promising results under few-shot prompting.
Key Takeaways
- →Technical imaging attributes like modality and plane detection are nearly solved by current multimodal AI models.
- →Diagnostic reasoning, especially subtype prediction, remains the most challenging task for AI models in neuroimaging.
- →Tumor classification proved most reliable while multiple sclerosis and rare abnormalities remain difficult to diagnose.
- →Few-shot prompting improves performance but significantly increases computational costs and latency.
- →Open-source MedGemma-1.5-4B approaches proprietary model performance under few-shot conditions while maintaining structured output.
#ai#healthcare#neuroimaging#benchmarking#medical-ai#multimodal-llm#diagnostic-ai#mri#machine-learning
Read Original →via arXiv – CS AI
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