Artificial intelligence for medicine: Progress, challenges, and perspectives
Tao Huang, Huiyu Xu, Haitao Wang, Haofan Huang, Yongjun Xu, Baohua Li, Shenda Hong, Guoshuang Feng, Shuyi Kui, Guangjian Liu, Dehua Jiang, Zhicheng Li, Ye Li, Congcong Ma, Chunyan Su, Wei Wang, Rong Li, Puxiang Lai, Jie Qiao
Abstract
<p>Artificial Intelligence (AI) has transformed how we live and how we think, and it will change how we practice medicine. With multimodal big data, we can develop large medical models that enables what used to unimaginable, such as early cancer detection several years in advance and effective control of virus outbreaks without imposing social burdens. The future is promising, and we are witnessing the advancement. That said, there are challenges that cannot be overlooked. For example, data generated is often isolated and difficult to integrate from both perspectives of data ownership and fusion algorithms. Additionally, existing AI models are often treated as black boxes, resulting in vague interpretation of the results. Patients also exhibit a lack of trust to AI applications, and there are insufficient regulations to protect patients’ privacy and rights. However, with the advancement of AI technologies, such as more sophisticated multimodal algorithms and federated learning, we may overcome the barriers posed by data silos. Deeper understanding of human brain and network structures can also help to unravel the mysteries of neural networks and construct more transparent yet more powerful AI models. It has become something of a trend that an increasing number of clinicians and patients will implement AI in their life and medical practice, which in turn can generate more data and improve the performance of models and networks. Last but not the least, it is crucial to monitor the practice of AI in medicine and ensure its equity, security, and responsibility.</p>