The rapid advancements in Large Language Models (LLMs) and Vision-Language Models (VLMs) have shown great potential in medical diagnostics, particularly in radiology, where datasets such as X-rays are paired with human-generated diagnostic reports. However, a significant research gap exists in the neuroimaging field, especially for conditions such as Alzheimer's disease, due to the lack of comprehensive diagnostic reports that can be utilized for model fine-tuning. This paper addresses this gap by generating synthetic diagnostic reports using GPT-4o-mini on structured data from the OASIS-4 dataset, which comprises 663 patients. Using the synthetic reports as ground truth for training and validation, we then generated neurological reports directly from the images in the dataset leveraging the pre-trained BiomedCLIP and T5 models. Our proposed method achieved a BLEU-4 score of 0.1827, ROUGE-L score of 0.3719, and METEOR score of 0.4163, revealing its potential in generating clinically relevant and accurate diagnostic reports.

翻译：大型语言模型（LLMs）与视觉语言模型（VLMs）的快速发展在医学诊断领域展现出巨大潜力，尤其在放射学中，X光等数据集常与人工生成的诊断报告配对使用。然而，在神经影像领域，特别是针对阿尔茨海默病等疾病，由于缺乏可用于模型微调的全面诊断报告，存在显著的研究空白。本文通过使用GPT-4o-mini对OASIS-4数据集（包含663名患者）的结构化数据生成合成诊断报告，以填补这一空白。利用这些合成报告作为训练和验证的基准真值，我们进一步借助预训练的BiomedCLIP和T5模型直接从数据集的图像中生成神经学报告。我们提出的方法取得了BLEU-4分数0.1827、ROUGE-L分数0.3719和METEOR分数0.4163，揭示了其在生成临床相关且准确的诊断报告方面的潜力。