The medical devices industry continues to be a hotbed of innovation, with activity driven by an increased need for homecare, preventative treatments, early diagnosis, reducing patient recovery times and improving outcomes, as well as the growing importance of 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: Treatment evaluation models.
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
Treatment evaluation models is a key innovation area in artificial intelligence
Treatment evaluation models refer to a treatment approach that is peer-reviewed for effectiveness. Artificial intelligence-based treatment models allow doctors to recommend accurate treatment options while saving time for both physicians and patients.
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 treatment evaluation models.
Key players in treatment evaluation models – 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’.
Smith & Nephew is one of the leading patent filers in the field of AI-treatment evaluation models. Some other key patent filers in the field include Stryker Corp and Johnson & Johnson.
In terms of application diversity, Motorika leads the pack, followed by AlterG and Fraunhofer-Gesellschaft zur Forderung der Angewandten Forschung eV. By means of geographic reach, Novartis holds the top position, followed by Johnson & Johnson and Smith & Nephew in the second and third spots, respectively.
The medical community is quickly adopting AI into its processes. AI will be a key driver in developing treatment evaluation models in the future. It will improve the process of data collection, leading to a better utilisation of available data for recommending treatment. Treatment evaluation models are increasingly used to measure the effectiveness of treatment pathways, both from a clinical point of view, but also in making an economic assessment of new treatments. Applying AI or Deep Learning techniques will lead to improved clinical outcomes but will also enable health chiefs to better deploy health resources, especially in times when there are increased budgetary constraints.
To further understand the key themes and technologies disrupting the medical devices industry, access GlobalData’s latest thematic research report on Medical Devices.
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- Source: https://www.medicaldevice-network.com/data-insights/innovators-ai-treatment-evaluation-models-medical-devices/
