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arxiv: 2605.03352 · v1 · submitted 2026-05-05 · 💻 cs.CV · cs.AI

Recognition: unknown

Can Multimodal Large Language Models Understand Pathologic Movements? A Pilot Study on Seizure Semiology

Chong Han, Colin McCrimmon, Jessica Pasqua, Lina Zhang, Mehmet Efe Lorasdagi, Peizheng Li, Prateik Sinha, Rajarshi Mazumder, Tonmoy Monsoor, Vwani Roychowdhury, Yuan Wang

Pith reviewed 2026-05-08 01:20 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords seizure semiologymultimodal large language modelspathologic movementsclinical video analysiszero-shot evaluationepilepsyexplainable AIILAE features
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The pith

Multimodal large language models recognize most seizure movement features in video without any special training.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tests whether general-purpose multimodal AI models can pick out specific pathological movements from clinical videos of seizures. It runs state-of-the-art models on 90 real recordings and measures performance against 20 standard medical features used by neurologists. The models beat purpose-built video classifiers on most features while also producing explanations that experts judge as mostly faithful to clinical reasoning. Targeted video edits such as face cropping and pose tracking further lift accuracy on half the features. This approach points to a practical route for using ready-made AI in specialized medical video review without first building large labeled datasets.

Core claim

State-of-the-art multimodal large language models achieve higher accuracy than fine-tuned CNN and ViT baselines on 13 of 18 ILAE-defined semiological features in zero-shot evaluation across 90 clinical seizure videos. Feature-targeted preprocessing steps improve results on 10 of 20 features, and expert raters find 94.3 percent of the models' explanations for correct predictions at least 60 percent faithful to epileptologist reasoning.

What carries the argument

Zero-shot MLLM evaluation on ILAE semiological features in seizure videos, combined with feature-targeted signal enhancement such as facial cropping, pose estimation, and audio denoising.

If this is right

  • General-purpose MLLMs offer a route to interpretable diagnostic assistance in epilepsy video monitoring.
  • Targeted preprocessing can adapt off-the-shelf models to clinical video tasks without full retraining.
  • AI explanations that align with expert reasoning could support review of long EEG-video recordings.
  • The same pipeline may reduce the data requirements for applying AI to other movement-based neurological assessments.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Performance may improve further if the models are tested on videos from multiple hospitals and seizure types.
  • Pairing MLLM outputs with simultaneous EEG signals could create stronger combined diagnostic tools.
  • The zero-shot strength suggests the method could be tried on other involuntary movement disorders such as tics or dystonia.
  • Real-time versions might eventually assist in emergency or home-monitoring settings where quick feature identification matters.

Load-bearing premise

The 90 seizure videos capture enough real-world variation in how seizures look and that the medical features can be spotted consistently by both models and experts.

What would settle it

A larger multi-center collection of seizure videos where the same MLLMs fall below the fine-tuned CNN and ViT baselines on most of the 18 features.

Figures

Figures reproduced from arXiv: 2605.03352 by Chong Han, Colin McCrimmon, Jessica Pasqua, Lina Zhang, Mehmet Efe Lorasdagi, Peizheng Li, Prateik Sinha, Rajarshi Mazumder, Tonmoy Monsoor, Vwani Roychowdhury, Yuan Wang.

Figure 1
Figure 1. Figure 1: MLLM-based medically induced involuntary movements recognition workflow with and without signal enhancement. (The person shown is an AI view at source ↗
Figure 2
Figure 2. Figure 2: MLLM justification faithfulness score distribution across 3 semiolog view at source ↗
read the original abstract

Multimodal Large Language Models (MLLMs) have demonstrated robust capabilities in recognizing everyday human activities, yet their potential for analyzing clinically significant involuntary movements in neurological disorders remains largely unexplored. This pilot study evaluates the capability of MLLMs for automated recognition of pathological movements in seizure videos. We assessed the zero-shot performance of state-of-the-art MLLMs on 20 ILAE-defined semiological features across 90 clinical seizure recordings. MLLMs outperformed fine-tuned Convolutional Neural Network (CNN) and Vision Transformer (ViT) baseline models on 13 of 18 features without task-specific training, demonstrating particular strength in recognizing salient postural and contextual features while struggling with subtle, high-frequency movements. Feature-targeted signal enhancement (facial cropping, pose estimation, audio denoising) improved performance on 10 of 20 features. Expert evaluation showed that 94.3 percent of MLLM-generated explanations for correctly predicted cases achieved at least 60 percent faithfulness scores, aligning with epileptologist reasoning. These findings demonstrate the potential of adapting general-purpose MLLMs for specialized clinical video analysis through targeted preprocessing strategies, offering a path toward interpretable, efficient diagnostic assistance. Our code is publicly available at https://github.com/LinaZhangUCLA/PathMotionMLLM.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 3 minor

Summary. The paper presents a pilot study evaluating the zero-shot performance of multimodal large language models (MLLMs) on recognizing 20 ILAE-defined semiological features from 90 clinical seizure videos. It reports that MLLMs outperform fine-tuned CNN and ViT baselines on 13 of 18 features, that feature-targeted signal enhancements (facial cropping, pose estimation, audio denoising) improve results on 10 of 20 features, and that expert review found 94.3% of MLLM explanations for correct predictions to be at least 60% faithful to epileptologist reasoning. Code is released publicly.

Significance. If the empirical claims hold after addressing dataset and statistical gaps, the work indicates that general-purpose MLLMs can be adapted via lightweight preprocessing for clinical video analysis of pathological movements, providing an interpretable alternative to task-specific models in epilepsy semiology. The public code release supports reproducibility.

major comments (3)
  1. [Methods and Results] Methods/Results: No per-feature positive example counts, patient demographics, seizure-type distribution, or cross-validation scheme are reported despite the small n=90. This is load-bearing for the headline claim of MLLM superiority on 13/18 features, as low-prevalence features could produce chance-level differences or reflect under-trained baselines rather than genuine capability.
  2. [Results] Results: The manuscript provides no statistical testing (e.g., paired tests, confidence intervals, or p-values) for the reported performance differences between MLLMs and baselines or for the signal-enhancement gains. With a pilot-scale dataset this omission prevents assessing whether observed advantages are reliable.
  3. [Abstract and Results] Abstract and Results: The comparison is stated as 13 of 18 features while enhancement results use 20 features; the manuscript must clarify the exact feature set, which features were excluded from the baseline comparison, and why.
minor comments (3)
  1. [Methods] Provide the exact protocol and inter-rater details for the expert faithfulness scoring of explanations.
  2. [Methods] Clarify whether the 90 videos include multiple seizures per patient and any steps taken to avoid data leakage.
  3. [Supplementary Material] Add per-feature prevalence or confusion matrices to the supplementary material to allow readers to interpret the reported accuracies.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed comments on our pilot study. We address each major comment below and will revise the manuscript to incorporate additional details, statistical analyses, and clarifications while preserving the integrity of the reported findings.

read point-by-point responses

  1. Referee: [Methods and Results] Methods/Results: No per-feature positive example counts, patient demographics, seizure-type distribution, or cross-validation scheme are reported despite the small n=90. This is load-bearing for the headline claim of MLLM superiority on 13/18 features, as low-prevalence features could produce chance-level differences or reflect under-trained baselines rather than genuine capability.

    Authors: We agree these details are essential for proper interpretation given the pilot scale. The revised manuscript will add a supplementary table reporting per-feature positive example counts, patient demographics (age, sex, epilepsy syndrome), and seizure-type distribution across the 90 videos. For the cross-validation scheme, we employed a single fixed 70/30 train/test split for the fine-tuned CNN and ViT baselines to ensure no data leakage and a direct comparison to zero-shot MLLMs; we will state this explicitly in Methods and discuss the small-sample limitation. We note that baselines were trained with standard augmentations and hyperparameters, but the added counts will allow readers to evaluate prevalence effects. revision: yes

  2. Referee: [Results] Results: The manuscript provides no statistical testing (e.g., paired tests, confidence intervals, or p-values) for the reported performance differences between MLLMs and baselines or for the signal-enhancement gains. With a pilot-scale dataset this omission prevents assessing whether observed advantages are reliable.

    Authors: We acknowledge the lack of statistical testing. In revision we will report 95% bootstrap confidence intervals for all accuracy and F1 scores, as well as for the MLLM-baseline differences and enhancement gains. We will apply McNemar's test for paired binary predictions on each feature (with Bonferroni correction) and paired t-tests or Wilcoxon tests for enhancement improvements. These will be added to Results and a new statistical methods subsection; we recognize that power will be limited for low-prevalence features but believe the tests will still aid assessment of reliability. revision: yes

  3. Referee: [Abstract and Results] Abstract and Results: The comparison is stated as 13 of 18 features while enhancement results use 20 features; the manuscript must clarify the exact feature set, which features were excluded from the baseline comparison, and why.

    Authors: We will revise the abstract, Methods, and Results to state that all 20 ILAE-defined features were evaluated for MLLMs and signal enhancements. Two features (automatisms and eye deviation) had fewer than five positive examples and were therefore excluded from baseline training and the 13/18 comparison to avoid unreliable models due to extreme imbalance; we will list the full 20-feature set, mark the two excluded features, and explain the exclusion criterion in a dedicated paragraph. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical evaluation with no derivations or self-referential predictions

full rationale

The paper conducts a pilot empirical study evaluating zero-shot MLLM performance on 20 ILAE semiological features from 90 seizure videos, comparing against fine-tuned CNN and ViT baselines, with optional preprocessing and expert faithfulness checks. No mathematical derivations, equations, parameter fitting to subsets, or predictions that reduce to inputs by construction are present. Claims rest on direct accuracy measurements and human ratings against external video data and ILAE definitions, not on any self-definitional loop or self-citation chain. The evaluation is self-contained and falsifiable via the public code and dataset.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Empirical study relying on standard machine learning evaluation; no free parameters, axioms, or new entities postulated.

pith-pipeline@v0.9.0 · 5575 in / 1161 out tokens · 79604 ms · 2026-05-08T01:20:51.777562+00:00 · methodology

discussion (0)

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Reference graph

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