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AI in Medical Imaging Diagnostics: Benchmarking 60+ Companies

Deep learning has revolutionized image recognition and analysis, making unprecedented performance leaps between 2010-2014. These rapid advancements enabled the development of automated, accurate, accessible, and cost-effective medical diagnostics. Over 60 entities including 40 new firms globally have set out to capitalize on these technological advances, seeking to commercialize AI-based diagnostics services in fields such as cancer and cardiovascular disease (CVD). IDTechEx forecasts the market for AI-enabled image-based medical diagnostics to exceed $3 billion by 2030.

AI radiology funding by company 2010-2019

In this article, IDTechEx examines the future market for image recognition AI in medical diagnostics. The article considers the progress thus far and assesses how each segment of the market is likely to evolve. Next, it considers the competitive landscape, examining investment patterns by disease area, company readiness levels by application, and the trends in focus areas. Finally, it provides an outlook about the future of this market.

This article draws from the brand new IDTechEx report, “AI in Medical Diagnostics 2020-2030: Image Recognition, Players, Clinical Applications, Forecasts”. It develops detailed quantitative benchmarks and examines the real status of technological capabilities today. It provides realistic roadmaps, showing what challenges the technology faces and how it is likely to evolve. Furthermore, it analyses the competitive landscape, identifying and reviewing more than 60 companies and leading research institutes. The strengths of the companies’ technology are benchmarked, as well as their product positioning, value proposition, and more.

The Rise of Image Recognition AI in Medical Diagnostics

The use of image visualization and limited recognition software in medical diagnostics started over 20 years ago. This technology had however nearly reached its performance limits when deep learning (DL) and convolutional neural networks (CNNs) were developed, heralding a step-change in the capability and performance of machine vision.

This progress demonstrates that image recognition AI technology can match or even exceed human-level performance (in terms of accuracy, sensitivity, and specificity) in many disease areas and on many imaging modalities. The technical threshold for the automation of these diagnostic tasks has already been reached, laying the groundwork for commercial growth in the short and long term.

This is shown in the market projections below. Here, the bars represent estimates and growth forecasts from IDTechEx for total scan volumes per disease type. This is essentially the addressable market for AI. Note that accelerated growth in scan volumes can be expected when the AI is widely available and when the imaging equipment itself is low cost – e.g., RGB camera vs CT or MRI scanner.

The continuous line shows market penetration forecasts. The chart shows the average value weighted across all disease categories. In its report, IDTechEx has developed a different penetration curve for each segment, reflecting the state of technological readiness, clinical testing stage, the added value over existing methods, and so on.

Note that AI algorithms are already deployed with notable volumes. Nonetheless, an inflection point – as an average across all categories- is expected to occur around 2023-2024. All in all, AI usage in medical image diagnostics is anticipated to grow by nearly 10,000% until 2040 whilst the global addressable market (scan volumes regardless of processing method) will grow by 50%.


Nitisha Dubey

I am a Journalist with a post graduate degree in Journalism & Mass Communication. I love reading non-fiction books, exploring different destinations and varieties of cuisines. Biographies and historical movies are few favourites.

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