Has multimodal learning delivered universal intelligence in healthcare? A comprehensive survey
Year: 2024
Type: article
Abstract: 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 because of data complementarity, comprehensive information fusion, 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 this 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 to promote multimodal fusion technologies in the domain, towards the goal of universal intelligence in healthcare.
Source: Information Fusion
Authors: Qika Lin, Yifan Zhu, Xin Mei, Ling Huang, Jingying Ma, Kai He, Zhen Peng, Erik Cambria, Mengling Feng
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,840
OpenAlex ID: W4404517435
