The rapid development of artificial intelligence has constantly reshaped the field of intelligent healthcare and medicine. As a vital technology, multimodal learning has increasingly garnered interest due to data complementarity, comprehensive modeling form, and great application potential. Currently, numerous researchers are dedicating their attention to this field, conducting extensive studies and constructing abundant intelligent systems. Naturally, an open question arises that has multimodal learning delivered universal intelligence in healthcare? To answer the question, we adopt three unique viewpoints for a holistic analysis. Firstly, we conduct a comprehensive survey of the current progress of medical multimodal learning from the perspectives of datasets, task-oriented methods, and universal foundation models. Based on them, we further discuss the proposed question from five issues to explore the real impacts of advanced techniques in healthcare, from data and technologies to performance and ethics. The answer is that current technologies have NOT achieved universal intelligence and there remains a significant journey to undertake. Finally, in light of the above reviews and discussions, we point out ten potential directions for exploration towards the goal of universal intelligence in healthcare.

翻译：人工智能的快速发展不断重塑智能医疗与医学领域。作为一项关键技术，多模态学习因其数据互补性、综合性建模形式及巨大应用潜力而日益受到关注。目前众多研究者正致力于该领域，开展了广泛研究并构建了丰富的智能系统。一个自然浮现的问题是：多模态学习是否已实现医疗领域的通用智能？为回答此问题，我们采用三个独特视角进行整体分析。首先，我们从数据集、任务导向方法和通用基础模型三个维度对医疗多模态学习的当前进展进行全面综述。在此基础上，我们进一步通过五个核心议题探讨该问题，从数据与技术到性能与伦理，深入解析先进技术对医疗领域的实际影响。结论是：当前技术尚未实现通用智能，仍有漫长道路需要探索。最后，基于上述综述与讨论，我们针对医疗通用智能目标提出十个值得探索的潜在研究方向。