The rise of image recognition AI in medical diagnostics
AI in medical imaging diagnostics: Benchmarking 60+ companies
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.
State of technological readiness
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%.