Artificial intelligence accelerates multi-modal biomedical process: A Survey

The abundance of artificial intelligence AI algorithms and growing computing power has brought a disruptive revolution to the smart medical industry. Its powerful data abstraction and representation capabilities enable the modeling of hundreds of millions of medical data, such as sub-Computed Tomography tumor identification, retinal lesion screening, and survival curve analysis. However, all of these applications demonstrate AI's use of unimodal data for specific tasks. In contrast, clinicians deal with multi-modal data from multiple sources when diagnosing, performing prognostic assessments, and deciding on treatment plans. These requirements have facilitated the development of multi-modal AI solutions and improved the performance of AI models in handling complex medical scenarios and data. In this paper, we provide an overview of the current state of the art and research in multi-modal biomedical AI, including applications, data, methods, and analytics. Additionally, we summarize potential research directions for multi-modal AI technologies in the future of healthcare.

Automated summary

The abundance of AI algorithms and computing power has revolutionized the smart medical industry, but most current applications are limited to unimodal data. To meet the demands of clinicians dealing with multi-modal data, the development of multi-modal AI solutions has become crucial.