Seonhee Cho

Choonghan Kim, Seonhee Cho, Joo Heung Yoon

Background: Large language models (LLMs) are gaining use in clinical settings, but their performance can suffer with incomplete radiology reports. We tested whether multimodal LLMs (using text and images) could improve accuracy and understanding in chest radiography reports, making them more effective for clinical decision support. Purpose: To assess the robustness of LLMs in generating accurate impressions from chest radiography reports using both incomplete data and multimodal data. Material and Methods: We used 300 radiology image-report pairs from the MIMIC-CXR database. Three LLMs (OpenFlamingo, MedFlamingo, IDEFICS) were tested in both text-only and multimodal formats. Impressions were first generated from the full text, then tested by removing 20%, 50%, and 80% of the text. The impact of adding images was evaluated using chest x-rays, and model performance was compared using three metrics with statistical analysis. Results: The text-only models (OpenFlamingo, MedFlamingo, IDEFICS) had similar performance (ROUGE-L: 0.39 vs. 0.21 vs. 0.21; F1RadGraph: 0.34 vs. 0.17 vs. 0.17; F1CheXbert: 0.53 vs. 0.40 vs. 0.40), with OpenFlamingo performing best on complete text (p<0.001). Performance declined with incomplete data across all models. However, adding images significantly boosted the performance of MedFlamingo and IDEFICS (p<0.001), equaling or surpassing OpenFlamingo, even with incomplete text. Conclusion: LLMs may produce low-quality outputs with incomplete radiology data, but multimodal LLMs can improve reliability and support clinical decision-making. Keywords: Large language model; multimodal; semantic analysis; Chest Radiography; Clinical Decision Support;

Seonhee Cho, Choonghan Kim, Jiho Lee et al.

Recent advancements in Large Multimodal Models (LMMs) have attracted interest in their generalization capability with only a few samples in the prompt. This progress is particularly relevant to the medical domain, where the quality and sensitivity of data pose unique challenges for model training and application. However, the dependency on high-quality data for effective in-context learning raises questions about the feasibility of these models when encountering with the inevitable variations and errors inherent in real-world medical data. In this paper, we introduce MID-M, a novel framework that leverages the in-context learning capabilities of a general-domain Large Language Model (LLM) to process multimodal data via image descriptions. MID-M achieves a comparable or superior performance to task-specific fine-tuned LMMs and other general-domain ones, without the extensive domain-specific training or pre-training on multimodal data, with significantly fewer parameters. This highlights the potential of leveraging general-domain LLMs for domain-specific tasks and offers a sustainable and cost-effective alternative to traditional LMM developments. Moreover, the robustness of MID-M against data quality issues demonstrates its practical utility in real-world medical domain applications.