Oversight AI is an emerging concept in radiology where the AI forms a
symbiosis with radiologists by continuously supporting radiologists in their
decision-making. Recent advances in vision-language pre-training sheds a light
on the long-standing problems of the oversight AI by the understanding of both
visual and textual concepts and their semantic correspondences. However, there
have been limited successes in the application of vision-language pre-training
in the medical domain, as the current vision-language models and learning
strategies for photographic images and captions are not optimal to process the
medical data that are usually insufficient in the amount and the diversity. To
address this, here we present medical X-VL, a self-supervised model tailored
for efficient vision-language pre-training that exploits cross attention in the
radiological images and reports' common feature space in a symmetric manner. We
experimentally demonstrate that the pre-trained medical X-VL model outperforms
the current state-of-the-art models in various vision-language tasks in medical
domains. We finally demonstrate practical clinical usages of our oversight AI
for monitoring human errors and in the diagnosis of newly emerging diseases,
which suggests the potential of an oversight AI model for widespread
applicability in different medical applications.

该研究介绍了一种适用于医学领域的自我监督视觉语言预训练模型 Medical X-VL，展示该模型在医学领域的视觉语言任务中的超越性能，并证明了在新疾病诊断和监测人类错误等方面，Oversight AI 具有广泛的应用潜力。