Who are the leading innovators of AI-assisted radiology in the tech industry?

The technology industry continues to be a hotbed of innovation, driven by technological advances, increased connectivity, the urgency to make companies more efficient and competitive in an ever-changing marketplace, and the growing importance of technologies such as: activities are promoted by Machine learning, deep learning, natural language processing, augmented and virtual reality. These technologies provide radiologists with advanced tools for image analysis, diagnosis, treatment planning and improved patient care outcomes. In the last three years alone, more than 3.6 million of his patents have been filed and granted in the tech industry, according to GlobalData’s report on “Innovation in Artificial Intelligence: AI-Assisted Radiology.”

However, not all innovations are the same, nor do they follow a constant upward trend. Instead, their evolution takes the form of an sigmoidal curve that reflects a typical life cycle from early emergence to accelerated introduction and finally to reaching stable maturity.

Identifying specific innovations, especially those in the emerging and accelerating stages, is essential to understanding the current level of adoption and the expected future trajectory and impact.

Over 300 innovations shape the tech industry

GlobalData’s Technology Foresights uses an innovation strength model built on over 2.5 million patents to plot the S-curve of the tech industry, with more than 300 areas of innovation shaping the industry’s future.

Within emerging The innovation stage, finite element simulations, ML-enabled blockchain networks, and generative adversarial networks (GANs) are disruptive technologies that are in the early stages of application and should be followed closely. Demand forecasting applications, intelligent embedded systems, and deep reinforcement learning are some of them. To accelerate An area of innovation where adoption is steadily increasing.in mature Innovative areas are wearable physiological monitors, smart lighting and smart climate control systems, which are now well established in the industry.

Artificial intelligence innovation S-curve in the tech industry

AI-assisted radiology is a major innovation area for artificial intelligence

AI-assisted radiology refers to the application of artificial intelligence (AI) technology to assist radiologists in tasks such as image analysis, diagnosis, and patient care. These systems leverage AI algorithms to help detect anomalies, speed up image review, and improve diagnostic accuracy and consistency. This technology reduces the workload of radiologists, allowing them to focus on more complex cases, ultimately improving overall efficiency and patient outcomes.

GlobalData’s analysis also reveals the companies at the forefront of each innovation area and assesses the potential scope and impact of patent activity across different applications and geographies. According to GlobalData, there are more than 870 companies, including technology vendors, established technology companies and emerging start-ups, committed to the development and application of AI-assisted radiology.

Leader in AI-Assisted Radiology – Disruptive Innovation in the Technology Industry

‘Application Diversity’ measures the number of different applications identified for each relevant patent and broadly divides companies into ‘niche’ or ‘diverse’ innovators.

“Geographic coverage” refers to the number of different countries in which each relevant patent is registered, reflecting the breadth of intended geographic application, ranging from “global” to “local” .

Number of patents related to AI-assisted radiology

Source: GlobalData Patent Analytics

Koninklijke Philips is one of the leading patent applicants in the field of AI-assisted radiology. The company’s patents cover a medical imaging system configured to link acquired images to markers or tags on an anatomical illustration based on spatial and anatomical data associated with the acquired images. there is

The medical imaging system can be further configured to generate a diagnostic report that includes an anatomical drawing that includes the markers. Diagnostic reports allow the user to select markers to view information associated with acquired images. Multiple images of him can be associated with one marker and/or multiple markers can be associated with one image of her. Generate a series of 2D/3D anatomical illustrations containing markers from multiple diagnostic reports to size, position, deform or interfere with organs, tissues and vessels.

Other prominent patent applicants in the AI-assisted radiology field include Siemens and Fujifilm.

In terms of geographic reach, Magic Leap leads the way, followed by Sung Kwang Medical Foundation and VPIX Medical. In terms of application diversity, Neurent Medical takes the top spot, followed by Masimo and Caris Life Sciences.

AI-assisted radiology has the potential to improve the efficiency, accuracy, and patient outcomes of medical imaging. This technology will transform the field by empowering radiologists to enable faster diagnosis, more accurate interpretation and informed decision-making, ultimately leading to enhanced healthcare delivery. have power

To better understand how artificial intelligence is transforming the tech industry, visit GlobalData’s latest Thematic Research Report on Artificial Intelligence (AI) – Thematic Intelligence.