LLaVA-NeXT-Med: Medical Multimodal Large Language Model

This paper introduces the LLaVA-NeXT-Med model, a medical multi-modal system that is pretrained and fine tuned using datasets incorporating LLaVA-Med data to enhance the performance of medical image processing and visual question answering (VQA). The purpose of this study was to evaluate the potential application of LLaVA-NeXT-Med in the medical field and to verify its effectiveness through a series of experiments. First, the LLaVA-NeXT and LLaVA-Med fusion data were used for pretraining, followed by visual fine tuning using the fusion dataset. Through offline experiments on three medical VQA datasets, the results show that LLaVA-NeXT-Med outperforms the original model on several evaluation indicators. The experimental results demonstrate the effectiveness of LLaVA-NeXT-Med in improving medical image analysis and VQA tasks.

Authors

Institutions

Publication Details

Published
2025-01-10
DOI
https://doi.org/10.1109/citsc64390.2025.00092
Citations
1
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
2.48
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

LLaVA-NeXT-Med: Medical Multimodal Large Language Model

Wu Huang, Yunfei Guo
1 citations
Topic Modeling
2.48
article

LLaVA-NeXT-Med: Medical Multimodal Large Language Model

Wu Huang, Yunfei Guo
article en
1 citations

Abstract

This paper introduces the LLaVA-NeXT-Med model, a medical multi-modal system that is pretrained and fine tuned using datasets incorporating LLaVA-Med data to enhance the performance of medical image processing and visual question answering (VQA). The purpose of this study was to evaluate the potential application of LLaVA-NeXT-Med in the medical field and to verify its effectiveness through a series of experiments. First, the LLaVA-NeXT and LLaVA-Med fusion data were used for pretraining, followed by visual fine tuning using the fusion dataset. Through offline experiments on three medical VQA datasets, the results show that LLaVA-NeXT-Med outperforms the original model on several evaluation indicators. The experimental results demonstrate the effectiveness of LLaVA-NeXT-Med in improving medical image analysis and VQA tasks.

Sichuan University (CN)
Openalex Percentile: Top 11%
Topic Modeling
2.48
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.