The healthcare industry continues to be a hotbed of innovation, with activity driven by telemedicine, real-time diagnostics, smart hospitals and access to digital therapies, as well as the growing importance of technologies such as artificial intelligence (AI), the Internet of Things (IoT), augmented reality (AR), robotics and data management practices. In the last three years alone, there have been over 106,000 patents filed and granted in the healthcare industry, according to GlobalData’s report on Artificial Intelligence in Healthcare: AI-assisted medical image analysis.
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.
200+ innovations will shape the healthcare industry
According to GlobalData’s Technology Foresights, which plots the S-curve for the healthcare industry using innovation intensity models built on over 443,000 patents, there are 200+ innovation areas that will shape the future of the industry.
Within the emerging innovation stage, drug delivery device security, microscopic image analysis models, and cellular imaging techniques are disruptive technologies that are in the early stages of application and should be tracked closely. Smart balloon catheters, automated immunoassay analysers, and AI-assisted MRI are some of the accelerating innovation areas, where adoption has been steadily increasing. Among maturing innovation areas are smart fitness training system and non-invasive physiological monitoring, which are now well established in the industry.
Innovation S-curve for artificial intelligence in the healthcare industry
AI-assisted medical image analysis is a key innovation area in artificial intelligence
AI is used to develop computer systems that can perform human-like tasks such as decision-making, visual perception and language translation. AI in diagnostic medical imaging is acclaimed. Its utilisation in medical image analysis software can handle large workloads as it can sort hundreds of images at once. Its accuracy and sensitivity in identifying imaging abnormalities can improve tissue detection and characterisation.
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 30 companies, spanning technology vendors, established healthcare companies, and up-and-coming start-ups engaged in the development and application of AI-assisted medical image analysis.
Key players in AI-assisted medical image analysis – a disruptive innovation in the healthcare 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 AI-assisted medical image analysis
Source: GlobalData Patent Analytics
Enlitic is the leading patent filer in the AI-assisted medical image analysis. Some other leading patent filers include Hologic and General Electric.
In terms of application diversity, Vektor Medical leads the pack, followed by Intuitive Surgical and Johnson & Johnson. With regards to geographic reach, Johnson & Johnson leads, followed by GC, Sony Group, and Toshiba.
In recent years, AI has changed medical imaging, including X-ray, ultrasound, and computerised tomography (CT). Numerous AI-based technologies have been developed to improve the automated interpretation and analysis of medical images. Deep learning, uncertain analysis, multi-objective optimisation, and machine learning are examples of analyses that employ these attributes frequently. The outcomes of these analyses allow physicians to examine more patients efficiently in lesser time.
To further understand how artificial intelligence is disrupting the healthcare industry, access GlobalData’s latest thematic research report on Artificial Intelligence (AI) in Healthcare.