Artificial Intelligence Market innovation Leading companies in supervised medical data analysis for the medical devices

The Artificial Intelligence market size was valued at $68.1 billion in 2021. The AI market is expected to grow at a CAGR of more than 14% during the forecast period (2021-2026). Growing popularity of the metaverse and its integration with artificial intelligence is expected to bode well fo

The need for homecare, preventative treatments, early diagnosis, shorter patient recovery times, and improved outcomes are driving activity in the medical devices industry, which is also a hotbed of innovation. Digitalization, machine learning, augmented reality, 5G, and other technologies are also becoming increasingly important. According to GlobalData's report on Artificial Intelligence Market in Medical Devices, the medical device industry has received over 450,000 patents in the last three years alone: supervised analysis of medical data.

However, not all innovations are created equal, and neither do they always advance. Instead, their typical lifecycle is represented by an S-shaped curve that depicts their early emergence, rapid adoption, and eventual stabilization and maturation.

It is essential to comprehend the current level of adoption as well as the likely future trajectory and impact of a given innovation to determine where it is on this journey, particularly in the emerging and accelerating stages.

According to GlobalData's Technology Foresights, which uses innovation intensity models based on more than 550,000 patents to plot the S-curve for the medical devices industry, there are more than 150 innovation areas that will shape the industry's future.

AI-assisted radiology, motion artifact analysis, and treatment evaluation models are disruptive technologies in the emerging innovation stage that should be closely monitored because they are still in the early stages of application. AI-assisted EHR/EMR, AI-assisted CT imaging, and MRI image smoothing are among the accelerating innovation areas whose adoption has been steadily rising. Computer-assisted surgeries and 3D endoscopy, which are now well-established in the industry, are two examples of maturing innovation areas.

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S-curve of innovation for artificial intelligence in the medical device industry: supervised medical data analysis The use of labelled datasets to train or "supervise" algorithms using machine learning to accurately classify medical data or predict outcomes is a key area of innovation in artificial intelligence.

In addition, GlobalData's analysis identifies the businesses that are at the forefront of each innovation field and evaluates the potential scope and impact of their patenting activity across various applications and regions. GlobalData says that 40 companies are working on the creation and application of supervised medical data analysis. These companies range from technology providers to established manufacturers of medical devices.

Key players in supervised medical data analysis—a disruptive innovation in the medical device industry Application diversity divides businesses broadly into "niche" or "diversified" innovators based on the number of distinct applications identified for each relevant patent.

The term "geographic reach" refers to the range of "global" to "local" countries in which each relevant patent is registered. It also reflects the scope of the intended geographic application.
In the field of supervised medical data analysis, BostonGene is one of the leading patent filers. NantWorks, Regeneron Pharmaceuticals, Aliphcom, and Takeda Pharmaceutical are all important patent filers in the field.

Waters leads the pack in terms of application diversity, followed by Calmark Sweden and Aliphcom. Globeimmune topped the list geographically, followed by DNAnudge and Takeda Pharmaceutical, which came in second and third, respectively.

There are a lot of labelled datasets in the healthcare industry, and supervised medical data analysis is probably the best short-term strategy for using these datasets to improve medical care. However, given that the data at hand is primarily biological in nature, this method is likely to be less effective at revealing the hidden relationships. Natural datasets are frequently unlabelled, and more appropriate for unaided or semi-regulated information examination, which is more probable, in the long haul, to uncover those new and novel connections found inside persistent information that can be utilized to impact step change enhancements in medical care.

Access the most recent thematic research report on Medical Devices from GlobalData to learn more about the key themes and technologies that are disrupting the medical device industry.


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