PUBLISHER: Grand View Research | PRODUCT CODE: 1433746
PUBLISHER: Grand View Research | PRODUCT CODE: 1433746
Multimodal AI Market Trends & Growth
The global multimodal AI market size is estimated to reach USD 10.89 billion by 2030 growing at a CAGR of 35.8% during the forecast period, according to a new report by Grand View Research, Inc. The increasing digitization of various data types, including images, text, audio, and video, has produced a demand for advanced technologies capable of processing and extracting meaningful insights from these varied sources. Multimodal artificial intelligence (AI) AI, with its ability to understand and analyze multiple modalities simultaneously, handles this need, boosting its adoption across numerous industries. In addition, the rising prevalence of data-rich applications, such as autonomous vehicles, virtual assistants, and augmented reality, have created new prospects for multimodal AI solutions as these applications require a complete understanding of complex data inputs, which is a notable strength of multimodal AI.
Multimodal AI applications in healthcare deliver transformative advantages through the enhancement of medical imaging analysis, disease diagnosis, and the development of personalized treatment plans. Integrating medical images with patient records and genetic data enables healthcare providers to attain a more accurate comprehension of individual patient health, facilitating the creation of customized treatment plans. This, in turn, results in improved patient outcomes and enhances operational efficiency within the healthcare sector. In November 2023, Tempus Labs, Inc. announced a strategic and multi-year research partnership with Bristol-Myers Squibb Company. This collaboration aims to accelerate the identification and validation of novel targets in specific cancer disease areas by leveraging multimodal datasets, computational methods, and patient-derived disease models, ensuring a faster and more confident validation process.
Multimodal AI harnesses the capabilities of diverse data types and computational resources accessible within cloud infrastructures. In cloud deployment, multimodal AI systems leverage computing resources and remote servers to process and analyze data from various sources concurrently. This approach seamlessly integrates different data modalities, including images, text, audio, and video, within a centralized cloud environment. The cloud-based deployment of multimodal AI offers scalability advantages, enabling organizations to adjust their computational resources according to demand effortlessly. In addition, cloud platforms operate on a pay-as-you-go model, reducing the upfront costs associated with deploying and maintaining multimodal AI infrastructure. This cost-efficiency appeals to companies of all sizes, as they can leverage advanced AI capabilities without substantial initial investments.