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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Published on: December 6, 2024

Multimodal Large Language Models in Medical Imaging: Current State and Future Directions.

Yoojin Nam^1,2, Dong Yeong Kim^1,3, Sunggu Kyung^1,4

¹Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea.

Korean Journal of Radiology

|September 28, 2025

Summary

This summary is machine-generated.

Multimodal large language models (MLLMs) show promise in radiology for tasks like report generation and diagnostics. However, challenges in data availability, transparency, and computational needs must be addressed for clinical integration.

Keywords:

Artificial intelligence Large language model Medical imaging Multimodal large language model

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Area of Science:

Artificial Intelligence in Medicine
Medical Imaging Analysis
Radiology Informatics

Background:

Multimodal large language models (MLLMs) are emerging as powerful AI tools in medicine, especially radiology.
They integrate large language models (LLMs) with diverse data, including clinical text and various radiological images (X-rays, CT, MRI).

Purpose of the Study:

To review the current capabilities and limitations of MLLMs in medicine, with a focus on radiology.
To outline key directions for future research and clinical integration of MLLMs.

Main Methods:

Review of current MLLM applications in radiology, including report generation, visual question answering, and diagnostic support.
Analysis of methods for multimodal integration and the impact of LLM advancements.

Main Results:

MLLMs demonstrate potential for automating preliminary radiology reports and aiding diagnostics.
Significant challenges include the scarcity of large-scale medical multimodal datasets, risk of hallucinated findings, lack of transparency, and high computational costs.

Conclusions:

Future research should focus on region-grounded reasoning, developing robust foundation models, and establishing safe clinical integration strategies.
Addressing current limitations is crucial for the widespread adoption of MLLMs in clinical radiology practice.