The medical devices industry continues to be a hotbed of innovation, with activity driven by increased need for homecare, preventative treatments, early diagnosis, reducing patient recovery times and improving outcomes, as well as a growing importance in technologies such as machine learning, augmented reality, 5G and digitalisation. In the last three years alone, there have been over 450,000 patents filed and granted in the medical devices industry, according to GlobalData’s report on Artificial Intelligence in Medical Devices: Supervised medical data analysis. Buy the report here.
However, not all innovations are equal and nor do they follow a constant upward trend. Instead, their evolution takes the form of an S-shaped curve that reflects their typical lifecycle from early emergence to accelerating adoption, before finally stabilising and reaching maturity.
Identifying where a particular innovation is on this journey, especially those that are in the emerging and accelerating stages, is essential for understanding their current level of adoption and the likely future trajectory and impact they will have.
150+ innovations will shape the medical devices industry
According to GlobalData’s Technology Foresights, which plots the S-curve for the medical devices industry using innovation intensity models built on over 550,000 patents, there are 150+ innovation areas that will shape the future of the industry.
Within the emerging innovation stage, AI-assisted radiology, motion artefact analysis, and treatment evaluation models are disruptive technologies that are in the early stages of application and should be tracked closely. MRI image smoothing, AI-assisted EHR/EMR, and AI-assisted CT imaging are some of the accelerating innovation areas, where adoption has been steadily increasing. Among maturing innovation areas are computer-assisted surgeries and 3D endoscopy which are now well established in the industry.
Innovation S-curve for artificial intelligence in the medical devices industry
Supervised medical data analysis is a key innovation area in artificial intelligence
Supervised medical data analysis refers to the use of labelled datasets that can be designed to train or “supervise” algorithms, using machine learning, into classifying medical data or predicting outcomes accurately.
GlobalData’s analysis also uncovers the companies at the forefront of each innovation area and assesses the potential reach and impact of their patenting activity across different applications and geographies. According to GlobalData, there are 40 companies, spanning technology vendors, established medical devices companies, and up-and-coming start-ups engaged in the development and application of supervised medical data analysis.
Key players in supervised medical data analysis – a disruptive innovation in the medical devices industry
‘Application diversity’ measures the number of different applications identified for each relevant patent and broadly splits companies into either ‘niche’ or ‘diversified’ innovators.
‘Geographic reach’ refers to the number of different countries each relevant patent is registered in and reflects the breadth of geographic application intended, ranging from ‘global’ to ‘local’.
Patent volumes related to supervised medical data analysis
Source: GlobalData Patent Analytics
BostonGene is one of the leading patent filers in supervised medical data analysis. Some other key patent filers in the field include Aliphcom, Takeda Pharmaceutical, Nantworks and Regeneron Pharmaceuticals.
In terms of application diversity, Waters leads the pack, followed by Calmark Sweden and Aliphcom, respectively. By means of geographic reach, Globeimmune held the top position, followed by DNAnudge and Takeda Pharmaceutical in the second and third spots, respectively.
Within healthcare, there are huge existing labelled datasets, and supervised medical data analysis is likely the best short-term approach to utilising these datasets to improve medical care. However, this approach is likely to be less able to unveil the hidden relationships in what is, at heart, data of a biological nature. Biological datasets are often unlabelled, and more suitable for unsupervised or semi-supervised data analysis, which is more likely, in the long term, to reveal those new and novel relationships found within patient data that can be used to effect step change improvements in healthcare.
To further understand the key themes and technologies disrupting the medical devices industry, access GlobalData’s latest thematic research report on Medical Devices.