What does multimodal AI refer to?

Multimodal AI refers to artificial intelligence systems that are capable of processing and analyzing data from various forms, such as text, images, audio, and video. This approach allows AI systems to integrate information from different modalities, enabling them to understand and generate insights that are richer and more nuanced than what could be achieved using a single type of data. By leveraging diverse data types, multimodal AI can improve performance across a range of applications, such as image captioning, video analysis, and conversational agents, where the context and meaning are derived from a combination of modalities.

The ability to analyze multiple forms of data simultaneously enhances the context and depth of understanding, making it a crucial aspect of advanced AI applications. For example, in a healthcare scenario, multimodal AI could analyze patient data together from medical imaging (like X-rays), electronic health records (clinical text), and audio (like patient-provider conversation), leading to more informed diagnoses and treatment plans.