Fusion-Augmented Large Language Models: Boosting Diagnostic Trustworthiness via Model Consensus

• URL: http://arxiv.org/abs/2510.16057v1
• Date: Thu, 16 Oct 2025 22:02:09 GMT
• Title: Fusion-Augmented Large Language Models: Boosting Diagnostic Trustworthiness via Model Consensus
• Abstract summary: This study presents a novel multi-model fusion framework leveraging two state-of-the-art large language models (LLMs), ChatGPT and Claude, to enhance the reliability of chest X-ray interpretation.<n>From the full CheXpert corpus of 224,316 chest radiographs, we randomly selected 234 radiologist-annotated studies to evaluate unimodal performance using image-only prompts.<n>A similarity-based consensus approach, using a 95% output similarity threshold, improved accuracy to 77.6%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study presents a novel multi-model fusion framework leveraging two state-of-the-art large language models (LLMs), ChatGPT and Claude, to enhance the reliability of chest X-ray interpretation on the CheXpert dataset. From the full CheXpert corpus of 224,316 chest radiographs, we randomly selected 234 radiologist-annotated studies to evaluate unimodal performance using image-only prompts. In this setting, ChatGPT and Claude achieved diagnostic accuracies of 62.8% and 76.9%, respectively. A similarity-based consensus approach, using a 95% output similarity threshold, improved accuracy to 77.6%. To assess the impact of multimodal inputs, we then generated synthetic clinical notes following the MIMIC-CXR template and evaluated a separate subset of 50 randomly selected cases paired with both images and synthetic text. On this multimodal cohort, performance improved to 84% for ChatGPT and 76% for Claude, while consensus accuracy reached 91.3%. Across both experimental conditions, agreement-based fusion consistently outperformed individual models. These findings highlight the utility of integrating complementary modalities and using output-level consensus to improve the trustworthiness and clinical utility of AI-assisted radiological diagnosis, offering a practical path to reduce diagnostic errors with minimal computational overhead.

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