With advances in generative artificial intelligence (AI), it is now possible to produce realistic-looking automated reports for preliminary reads of radiology images. This can expedite clinical workflows, improve accuracy and reduce overall costs. However, it is also well-known that such models often hallucinate, leading to false findings in the generated reports. In this paper, we propose a new method of fact-checking of AI-generated reports using their associated images. Specifically, the developed examiner differentiates real and fake sentences in reports by learning the association between an image and sentences describing real or potentially fake findings. To train such an examiner, we first created a new dataset of fake reports by perturbing the findings in the original ground truth radiology reports associated with images. Text encodings of real and fake sentences drawn from these reports are then paired with image encodings to learn the mapping to real/fake labels. The utility of such an examiner is demonstrated for verifying automatically generated reports by detecting and removing fake sentences. Future generative AI approaches can use the resulting tool to validate their reports leading to a more responsible use of AI in expediting clinical workflows.

翻译：随着生成式人工智能（AI）的进步，目前已经可以生成外观逼真的自动化报告，用于放射学影像的初步解读。这有助于加快临床工作流程、提高准确性并降低整体成本。然而，众所周知，此类模型常常会产生"幻觉"，导致生成报告中出现虚假发现。本文提出了一种利用相关影像对AI生成报告进行事实核查的新方法。具体而言，我们开发的核查器通过学习影像与描述真实或潜在虚假发现的句子之间的关联，来区分报告中的真实与虚假句子。为训练此类核查器，我们首先通过扰动与影像关联的原始真实放射学报告中的发现，创建了新的虚假报告数据集。然后，将这些报告中提取的真实与虚假句子的文本编码与影像编码配对，以学习到真实/虚假标签的映射。通过检测并移除虚假句子来验证自动生成报告的有效性，验证了此类核查器的实用价值。未来的生成式AI方法可利用此工具验证其报告，从而在加速临床工作流程中更负责任地应用AI。