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.
