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: Microscopic image analysis 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.
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
Microscopic image analysis models is a key innovation area in artificial intelligence
Microscopic image analysis models are used for quantifying biological samples, such as cell count. The models use techniques as per the data to be acquired, such as three-dimensional data which consists of series of images corresponding to focal planes in the sample tissue or time-series data which consist of series of images corresponding to sequence of time instances imaging a focal plane. Microscopic image analysis models have replaced conventional imaging methods as they are usually fully or semi-automated and thus provide microscopic information easily.
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 60+ companies, spanning technology vendors, established healthcare companies, and up-and-coming start-ups engaged in the development and application of microscopic image analysis models.
Key players in microscopic image analysis models – 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 microscopic image analysis models
Source: GlobalData Patent Analytics
Heartflow is one of the leading patent filers in the field of microscopic image analysis models. Some other key patent filers in the field include Microsoft and Fujifilm.
In terms of application diversity, X Development leads the pack, followed by Light AI and Yuyama. By means of geographic reach, ASTR holds the top position, followed by Transmural Biotech and Yuyama in the second and third spots, respectively.
Microscopic image analysis models involve the use of microscopes and computers and do not require whole process to be done manually, thus reducing human labour. Further developments in the field of microscopic image analysis would allow the use of artificial intelligence and machine learning to analyse and interpret tissue samples more clearly. This would allow better study of effects of modifications applied to cells and tissues.
To further understand how artificial intelligence is disrupting the healthcare industry, access GlobalData’s latest thematic research report on Artificial Intelligence (AI) in Healthcare.