Over 60 groups including 40 new firms globally are seeking to commercialize medical AI diagnostics services in fields such as cancer and cardiovascular disease (CVD).

Deep learning (DL) and convolutional neural networks (CNNs) artifical intelligence algorithms have enabled a step-change in the capability and performance of machine vision for diagnosis 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.

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

The most active and crowded market for image recognition AI, in terms of the number of players, is cancer, accelerating triage or improving diagnostic. Within this category, breast, lung, and skin cancer have been the main focus. 

Respiratory diseases and CVD each make up about a fifth of the company landscape. Covid-19 provided a boost for firms active in respiratory diseases as detection is largely based on algorithms built for respiratory disease detection. Companies in this sector were quick to adapt their existing software to detect signs of Covid-19 as it provided them publicity at a time when overwhelmed hospitals were desperate for any kind of support.

In terms of geography, the US is emerging as the central hub of medical AI image recognition technology. The annual scan volume in the USA per 1000 population is 3 to 5  times higher that of other countries. Interest in image recognition medical AI technology has also soared in the last decade and this is reflected by the level of investments it has generated. Combined investments since 2017 are over 200 percent higher than the total since the start of the decade, topping $2.2bn according to IDTechEx. 

However the days of leaps in performance of image recognition are over, barring radical innovation in algorithm techniques. The gains in precision, recall and other metrics will be incremental so the emphasis has shifted to other points such as  robust data acquisition and learning loops with partners.

While there is a spread in what different algorithms are offering, most are positioned as decision support tools. As a minimum, they need to detect the anomaly of interest. In some cases, they offer detailed pixel-level segmentation. The evolution is now to provide further information and explanations alongside object detection and instant segmentation. Some are even aiming to suggest treatment options, although this is generally further down the line. In short, the goal is to raise the AI complexity beyond object detection.

Furthermore, the algorithms today offer what humans do, but may do so faster and/or better, thus unleashing the automation wave. In the future, with more digitization of patient data, more data fusion can be expected, perhaps enabling AI to offer insights beyond human capability. This could be a game-changer says IDTechEx.

Scale will also be important. This gives more access to data, which increases algorithm accuracy, versatility, and applicability and creates a one-stop-shop proposition. Larger technical teams that can aid the on-site into-work-flow integration process, which in turn boosts installed base and acts a lock-in mechanism. 

The report “AI in Medical Diagnostics 2020-2030: Image Recognition, Players, Clinical Applications, Forecasts” is at

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