mlsanityData to 14 Oct 2026

Zero-Shot Multi-modal Large Language Model v.s. Supervised Deep Learning: A Comparative Study on CT-Based Intracranial Hemorrhage Subtyping

arXiv 2505.09252 ↗First posted 14 May 2025cs.CVcited by 1

On the map: Multimodal LLMs / vision-language models → Medical vision-language models

Abstract (arXiv)

Introduction: Timely identification of intracranial hemorrhage (ICH) subtypes on non-contrast computed tomography is critical for prognosis prediction and therapeutic decision-making, yet remains challenging due to low contrast and blurring boundaries. This study evaluates the performance of zero-shot multi-modal large language models (MLLMs) compared to traditional deep learning methods in ICH binary classification and subtyping. Methods: We utilized a dataset provided by RSNA, comprising 192 NCCT volumes. The study compares various MLLMs, including GPT-4o, Gemini 2.0 Flash, and Claude 3.5 Sonnet V2, with conventional deep learning models, including ResNet50 and Vision Transformer. Carefully crafted prompts were used to guide MLLMs in tasks such as ICH presence, subtype classification, localization, and volume estimation. Results: The results indicate that in the ICH binary classification task, traditional deep learning models outperform MLLMs comprehensively. For subtype classification, MLLMs also exhibit inferior performance compared to traditional deep learning models, with Gemini 2.0 Flash achieving an macro-averaged precision of 0.41 and a macro-averaged F1 score of 0.31. Conclusion: While MLLMs excel in interactive capabilities, their overall accuracy in ICH subtyping is inferior to deep networks. However, MLLMs enhance interpretability through language interactions, indicating potential in medical imaging analysis. Future efforts will focus on model refinement and developing more precise MLLMs to improve performance in three-dimensional medical image processing.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Medical vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 11 of its 51 references that are in the map. First posted on arXiv 14 May 2025.

Builds on

11 of 51 references.

Closest ancestors · 5

Same direction, newest first

  1. 2025EndoChat: Grounded Multimodal Large Language Model for Endoscopic Surgery20 Jan
  2. 2024GMAI-VL & GMAI-VL-5.5M: A Large Vision-Language Model and A Comprehensive Multimodal Dataset Towards General Medical AI21 Nov
  3. 2024Medical Adaptation of Large Language and Vision-Language Models: Are We Making Progress?6 Nov
  4. 2024E3D-GPT: Enhanced 3D Visual Foundation for Medical Vision-Language Model18 Oct
  5. 2023BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks26 May

Other references · 6

  1. 2023MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGIcited by 2,080
  2. 2024A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applicationscited by 431
  3. 2023BHSD: A 3D Multi-Class Brain Hemorrhage Segmentation Datasetcited by 27
  4. 2023SAMIHS: Adaptation of Segment Anything Model for Intracranial Hemorrhage Segmentationcited by 5
  5. 2021Semi-supervised learning for generalizable intracranial hemorrhage detection and segmentationcited by 3
  6. 2023Improving Automated Hemorrhage Detection in Sparse-view Computed Tomography via Deep Convolutional Neural Network based Artifact Reductioncited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026NeuroVLM-Bench: Evaluation of Vision-Enabled Large Language Models for Clinical Reasoning in Neurological Disorderscited by 1

Similar, not cited

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  3. 2025LLaVA-RadZ: Can Multimodal Large Language Models Effectively Tackle Zero-shot Radiology Recognition?
  4. 2026Lost in the Hype: Revealing and Dissecting the Performance Degradation of Medical Multimodal Large Language Models in Image Classification
  5. 2026Adapting 2D Multi-Modal Large Language Model for 3D CT Image Analysis
  6. 20253D Foundation Model for Generalizable Disease Detection in Head Computed Tomography
  7. 2025A Multi-agent Large Language Model Framework to Automatically Assess Performance of a Clinical AI Triage Tool
  8. 2024On Large Visual Language Models for Medical Imaging Analysis: An Empirical Study