Multimodal AI is rapidly shifting from the research and development stage to implementation, thus giving a huge boost to the multimodal AI market. Unlike traditional AI, which relies on a single type of data such as text or images, multimodal AI has the ability to process multiple types of data at the same time, including text, images, video, audio, sensor data, and structured data. This gives multimodal AI immense value in areas such as the healthcare, retail, and security industries, where real-world data is complex in nature.
Healthcare: Better Diagnosis, Faster Decisions, and Safer Care
Multimodal AI is also making its presence felt in the healthcare industry, which is resulting in greater accuracy and efficiency in the industry. It has the ability to combine different aspects of the healthcare industry, such as images, electronic health records, lab results, and patient history, to arrive at a greater accuracy level than a single AI model.
In 2024, Google DeepMind and University College London conducted research on multimodal medical AI models that combined medical images, clinical text, and patient data to improve diagnostic reasoning and reduce the risk of errors in complex situations. The results revealed that multimodal models performed better than image or clinical text-based models.
(Source: Arxiv)
Retail: Personalization, Loss Prevention, and Smart Stores
Retailing uses multimodal AI by integrating computer vision from overhead cameras, shelf weight sensors, and transaction information to enable Just Walk Out (JWO) technology, which facilitates cashier less shopping, accurate item recognition, and immediate digital receipts.
Amazon expanded JWO to over 170 third-party sites in airports, stadiums, and campuses in the U.S., the U.K., Australia, and Canada in 2024, doubling the number from before, while a new multimodal foundation model increased the ability to handle difficult scenarios through self-supervised learning from 3D store maps.
(Source: Intotheminds)
Security: Smarter Threat Detection and Risk Prevention
Multimodal AI is being used in security systems to minimize false positives and maximize the detection of actual threats by cross-validating video, radar, sensors, and behavior patterns, outperforming single-modal cameras or motion sensors.
For instance, in 2024, London Heathrow Airport enhanced its Genetec Security Center terminal solution by incorporating more than 9,000+ cameras, access control, video analytics, and sensors to offer real-time threat analysis, optimize passenger flow, and react to incidents with few false positives via multi-signal fusion and human analysis.
(Source: Benchmarkmagazine)
Why Multimodal AI Is Scaling So Fast
There are a number of structural elements that are pushing the adoption of multimodal AI. First, the fact is that organizations are already generating enormous amounts of text, images, video, and sensor data, and multimodal AI enables the use of all these sources of information together, rather than independently. Second, the development of GPUs and cloud computing makes it possible to train large models and process in real-time. Third, the major technology companies are working on multimodal foundation models that simplify development complexity and costs. Finally, multimodal AI provides greater accuracy, trust, and explainability, which is important in regulated industries such as healthcare and security.
Conclusion
Multimodal AI is gaining popularity as it imitates the way the actual world operates, where different forms of data are being combined to provide a more accurate outcome. The medical sector requires different forms of medical data, retail requires behavioral data, and security requires the verification of different signals. As the processing power and data integration capabilities increase, multimodal AI is becoming an essential element of the digital infrastructure, thus fueling the multimodal AI market growth.
FAQs
- What is multimodal AI?
Ans: Multimodal AI refers to artificial intelligence that is able to process and understand multiple types of data simultaneously, such as text, images, video, audio, and sensor data. - Why is multimodal AI better than traditional AI?
Ans: This is because multimodal AI is able to make more accurate and reliable decisions than single-source AI systems because it is able to use context from multiple sources of data. - Where is multimodal AI used today?
Ans: The top adoption areas for multimodal AI include healthcare diagnostics, retail personalization, and security systems. - Is multimodal AI used in real systems or just research?
Ans: Multimodal AI is already being used in hospitals, retail stores, airports, and smart city infrastructure around the world. - What industries will adopt multimodal AI next?
Ans: The top emerging adoption areas for multimodal AI include finance, manufacturing, logistics, defense, and education.
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