Has Multimodal Learning Delivered Universal Intelligence in Healthcare? A Comprehensive Survey
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914361973407744 |
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| author | Lin, Qika Zhu, Yifan Mei, Xin Huang, Ling Ma, Jingying He, Kai Peng, Zhen Cambria, Erik Feng, Mengling |
| author_facet | Lin, Qika Zhu, Yifan Mei, Xin Huang, Ling Ma, Jingying He, Kai Peng, Zhen Cambria, Erik Feng, Mengling |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_12880 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Has Multimodal Learning Delivered Universal Intelligence in Healthcare? A Comprehensive Survey Lin, Qika Zhu, Yifan Mei, Xin Huang, Ling Ma, Jingying He, Kai Peng, Zhen Cambria, Erik Feng, Mengling Artificial Intelligence Multimedia 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. |
| title | Has Multimodal Learning Delivered Universal Intelligence in Healthcare? A Comprehensive Survey |
| topic | Artificial Intelligence Multimedia |
| url | https://arxiv.org/abs/2408.12880 |