Computer-assisted diagnostic and prognostic systems of the future should be capable of simultaneously processing multimodal data. Multimodal deep learning (MDL), which involves the integration of multiple sources of data, such as images and text, has the potential to revolutionize the analysis and interpretation of biomedical data. However, it only caught researchers' attention recently. To this end, there is a critical need to conduct a systematic review on this topic, identify the limitations of current work, and explore future directions. In this scoping review, we aim to provide a comprehensive overview of the current state of the field and identify key concepts, types of studies, and research gaps with a focus on biomedical images and texts joint learning, mainly because these two were the most commonly available data types in MDL research. This study reviewed the current uses of multimodal deep learning on five tasks: (1) Report generation, (2) Visual question answering, (3) Cross-modal retrieval, (4) Computer-aided diagnosis, and (5) Semantic segmentation. Our results highlight the diverse applications and potential of MDL and suggest directions for future research in the field. We hope our review will facilitate the collaboration of natural language processing (NLP) and medical imaging communities and support the next generation of decision-making and computer-assisted diagnostic system development.

翻译：未来的计算机辅助诊断和预后系统应能同时处理多模态数据。多模态深度学习（MDL）通过整合图像、文本等多种数据源，有望彻底改变生物医学数据的分析与解释方式。然而，该领域直至近期才引起研究者关注。为此，亟需对该主题进行系统性综述，明确当前工作的局限性并探索未来方向。本范围综述旨在全面概述该领域现状，聚焦生物医学图像与文本联合学习（因这两者是MDL研究中最为常见的数据类型），厘清关键概念、研究类型及研究空白。本研究梳理了多模态深度学习在五类任务中的当前应用：（1）报告生成，（2）视觉问答，（3）跨模态检索，（4）计算机辅助诊断，（5）语义分割。研究结果凸显了MDL的多样化应用与潜力，并为该领域未来研究方向提供了建议。我们期望本综述能促进自然语言处理（NLP）与医学影像领域的协同合作，推动下一代决策支持及计算机辅助诊断系统的开发。