# Comprehensive Evaluation of Multimodal AI Models in Medical Imaging Diagnosis: From Data Augmentation to Preference-Based Comparison

arXiv: https://arxiv.org/abs/2412.05536 · HTML: https://mlsanity.com/paper/2412.05536/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 21 references that are in the map.
- First posted on arXiv 7 Dec 2024.

## Builds on (most cited first, 7 of 7)

- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day](https://mlsanity.com/paper/2306.00890/) (2023)
- [CXR-LLAVA: a multimodal large language model for interpreting chest X-ray images](https://mlsanity.com/paper/2310.18341/) (2023)
- [Detecting Misinformation in Multimedia Content through Cross-Modal Entity Consistency: A Dual Learning Approach](https://mlsanity.com/paper/2409.00022/) (2024)
- [Let Community Rules Be Reflected in Online Content Moderation](https://mlsanity.com/paper/2408.12035/) (2024)
- [Leveraging Large Language Models to Enhance Personalized Recommendations in E-commerce](https://mlsanity.com/paper/2410.12829/) (2024)

## Built on by (most cited first, 10 of 10)

- [Leveraging Convolutional Neural Network-Transformer Synergy for Predictive Modeling in Risk-Based Applications](https://mlsanity.com/paper/2412.18222/) (2024)
- [Machine Learning Techniques for Pattern Recognition in High-Dimensional Data Mining](https://mlsanity.com/paper/2412.15593/) (2024)
- [Longitudinal Flow Matching for Trajectory Modeling](https://mlsanity.com/paper/2510.03569/) (2025)
- [Dynamic Scheduling Strategies for Resource Optimization in Computing Environments](https://mlsanity.com/paper/2412.17301/) (2024)
- [Applications of Large Models in Medicine](https://mlsanity.com/paper/2502.17132/) (2025)
- [Multi-Scale Transformer Architecture for Accurate Medical Image Classification](https://mlsanity.com/paper/2502.06243/) (2025)
- [Computer Vision-Driven Gesture Recognition: Toward Natural and Intuitive Human-Computer](https://mlsanity.com/paper/2412.18321/) (2024)
- [Deep Learning in Image Classification: Evaluating VGG19's Performance on Complex Visual Data](https://mlsanity.com/paper/2412.20345/) (2024)
- [When Harmful Content Gets Camouflaged: Unveiling Perception Failure of LVLMs with CamHarmTI](https://mlsanity.com/paper/2512.03087/) (2025)
- [Physics-Grounded Disentangled Flow Modeling for Brain Disease Progression Trajectory](https://mlsanity.com/paper/2606.28630/) (2026)

## Abstract (arXiv)

This study introduces an evaluation framework for multimodal models in medical imaging diagnostics. We developed a pipeline incorporating data preprocessing, model inference, and preference-based evaluation, expanding an initial set of 500 clinical cases to 3,000 through controlled augmentation. Our method combined medical images with clinical observations to generate assessments, using Claude 3.5 Sonnet for independent evaluation against physician-authored diagnoses. The results indicated varying performance across models, with Llama 3.2-90B outperforming human diagnoses in 85.27% of cases. In contrast, specialized vision models like BLIP2 and Llava showed preferences in 41.36% and 46.77% of cases, respectively. This framework highlights the potential of large multimodal models to outperform human diagnostics in certain tasks.