Market and Technology Trends

Artificial Intelligence for Medical Imaging 2020

By Yole Intelligence — Jan 2020

With the emergence of AI in imaging, the medical industry and the radiology profession have begun to dramatically change.

ARTIFICIAL INTELLIGENCE ALGORITHMS: THREATS AND OPPORTUNITIES

Why is artificial intelligence (AI) a term we find regularly in the medical industry? In particular, why is this technology at the center of debates in the medical imaging market? Because this technology has the potential to change all of our diagnostics and treatment procedures to enable more personalized and effective medicine.

Artificial intelligence is based on the training of algorithms. Deep learning is a type of AI technology based on artificial neural networks which can detect more precise details in the data. This technology has initially been implemented for recognition models and is specialized for the study of images.

Radiology is mutating with the adoption of deep learning models for the recognition of lesions in the body, to prioritize cases for the direct treatment of patients at risk, to predict the evolution of pathologies. Furthermore, AI affects all the imaging modalities in particular Magnetic Resonance Imaging (MRI), Computed Tomography (CT) scanning, X-rays and ultrasound imaging. These are the ones at the center of our study.

Not every type of modality requires the same types of algorithms. In fact, modalities can be organized into two types of procedures: quality procedures, which include MRI and CT scans, and fast imaging procedures, which include ultrasound and X-rays.

The professionals’ needs depend highly on the imaging modality used.

On the one hand, MRI and CT scans are intensive procedures able to acquire high quality images. With the addition of annotations on the images, the model can reach very high accuracy to classify pathologies or to segment objects. Furthermore, the execution speed of the model does not need to be very fast, as the imaging procedure is usually long. On the other hand, models trained on ultrasound images are in need of very fast execution to be able to process real-time images. Those models are then used to detect abnormalities faster and prioritize cases, implying an important productivity gain.

The application of the models empowered by AI can be classified as:

Screening models: in charge of the detection of abnormalities
Diagnostic models: in charge of the evaluation of the disease
Treatment planning models: able to predict the most pertinent treatment according to the pathology and the physical condition of the patient.

The value generated by the use of such models in hospitals depends on their applications.

The Artificial Intelligence for Medical Imaging 2020 report evaluates the impact of deep learning on the medical imaging field.

AI FOR MEDICAL IMAGING: A FAST GROWING MARKET. WORTH $2.9B IN 2025, ITS VALUE WILL MULTIPLY BY 15-FOLD IN FIVE YEARS

The medical imaging field is very restrictive for the development and deployment of AI algorithms. The constraints are either related to medical data or to regulations.

Medical data and more specifically medical imaging depend on the parametrization of the imaging machine and on its vendor. The development of the interoperability of the models is highly challenging for software companies and hospitals.

To predict the evolution of a pathology, researchers must do longitudinal studies, requiring access to medical images of a patient over time, years in general. Inconsistencies in most of the medical data make the development of accurate and complex studies challenging.

Furthermore, regulations regarding data privacy make data access more complex for every player in the ecosystem. Annotated images are the fuel for AI, and access to them is often restricted, as well as other interesting data concerning the patients’ age, gender, size and so on.

Although the medical field is highly supervised, the AI market in this field will grow rapidly, first with the introduction of the AI products from original equipment manufacturer (OEMs), in particular Siemens and Philips, and secondly with the introduction of diagnostic algorithms into the market from 2021 onwards. The large productivity gain linked to the use of such models is valuable, enabling hospitals to more effectively diagnose and treat patients.

Yole Développement’s analysts estimate that the total market in 2025 for software generated revenues through the sale of AI tools will reach $2.9B with a Compound Annual Growth Rate for 2019 to 2025 (CAGR_2019-2025) of 36%, shared between the main applications: improved image capture, noise reduction, image reconstruction, screening, diagnostic and treatment planning.

AN EVER-MOVING ECOSYSTEM WITH DIVERSE MARKET POSITIONS AND STRUCTURES

The artificial intelligence for medical imaging market is made up of a myriad of start-ups, many of which being spin-offs from universities. It highlights the fact that this market is driven by medical research, as well as the value it can generate for hospitals with regards to all the hardware, and cloud companies as well as OEMs that revolve around it.

Each software company is highly specific, providing AI algorithms for dedicated applications and modalities. They are able to sell their products directly to hospitals or through two different types of players: marketplaces such as TeraRecon, which are integrating those algorithms into the radiologist’s workflow, or through OEMs such as Siemens, which are optimizing those models on their medical imaging machines.

In fact, OEM machine manufacturers and marketplaces, which are software companies, are offering the approximately the same product. Which type of player will be in the stronger position in the coming years?

Yole Développement’s report provides some answers to these key questions as well as a detailed analysis of the different strategies of these players.

Key features of the report

Artificial intelligence (AI) technologies used in medical imaging applications
AI software companies’ strategies
Evolution of business models and positioning
Cloud and on-premises computing for AI
Regulations and constraints inherent to the medical imaging field
Ecosystems, market forecasts and major trends

Objectives of the report

Provide an overview of the market of AI in the field of medical imaging in terms of number of algorithms deployed and the value they generate for every player involved, at medical device level, AI platform level, and algorithm development level along with the understanding of the ecosystem, technologies used, strategic positioning, and how these will evolve in the coming years
Present the current market data and forecasts depending on the modality studied (MRI, CT scans, X-rays and Ultrasound) and its application in the patient diagnostic process (noise reduction, screening or diagnostics), in dollar value and volume of images analyzed
Identify where the opportunities lie for each type of player along with a detailed description of the regulations and constraints of this field. In addition to the current overview, an introduction of the evolution of those regulations in the coming years is presented

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