AI IN MEDICAL IMAGING MARKET SIZE AND SHARE ANALYSIS - GROWTH TRENDS AND FORECASTS (2024-2031)

AI in Medical Imaging Market Size and Trends

Global AI in medical imaging market is estimated to be valued at USD 1.21 Bn in 2024 and is expected to reach USD 9.60 Bn by 2031, exhibiting a compound annual growth rate (CAGR) of 34.4% from 2024 to 2031.

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The global AI in medical imaging market is expected to witness significant growth during the forecast period. Growing applications of AI in medical imaging for various disease diagnosis and image analysis is expected to drive the market. AI assistance in medical imaging helps in faster and more accurate diagnosis by analyzing large amounts of patient data. Adoption of AI tools like deep learning and machine learning for medical image analysis is gaining traction among healthcare providers.

Market Driver - Growth in volume of medical imaging data

Modern medical imaging procedures have exploded in the past few years due to development and widespread adoption of technologies like CT, MRI, ultrasound, and others. These advanced imaging tools have enabled doctors to peek inside human body in great detail to detect diseases. However, rising number of imaging procedures can lead to increase in volume of medical images being generated every day. A large hospital may easily generate terabytes of imaging data on daily basis from various modalities. Moreover, recent advancements have enabled higher resolution images taking up more storage. Managing and analyzing this huge imaging data is a monumental task for healthcare providers.

According to research,  single CT scan can generate over 500 images totalling around 50 MB data size. With millions of scans taken yearly across hospitals and diagnostic centers, accumulating imaging archives have swelled to petabytes of data. MRI scan generates multiple sequences of images totalling 100s of MB data per patient. Top academic medical centers with level 1 trauma facilities may have 50+ CT and MRI scanners that continuously add to imaging archives. Furthermore, rising lifestyle diseases and aging population can lead to increase in number of scans in the near future.

While storage of gigantic imaging archives is manageable with advanced systems, analyzing this data overload manually is nearly impossible. Even specialized radiologists cannot practically review entire previous scans of all patients coming for follow ups or second opinion. Thus, artificial intelligence plays a transformational role in this. Various AI algorithms are being developed and applied to sift through past images, detect subtle patterns and provide computer aided diagnosis. AI can even harvest quantitative data from images pave way for predictive, preventive and participatory healthcare. This has vastly expanded the realm of possibilities for precision medicine through data-driven insights. AI helps to overcome the limitations caused by constant growth in size and complexity of medical imaging archives.

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Increasing adoption of AI-based medical imaging systems in hospitals and diagnostic centers

Due to proven success of AI in medical imaging applications, there has been rise in adoption across hospitals and diagnostic centers. AI demonstrates the ability to augment and enhance radiologists' expertise through capabilities like automatic analysis, prioritization and quantification of images. Early adopters have reported improved efficiency, reduced workload pressures and better consistency in reporting. AI excels in analysis of huge volume of previous scans that can beyond human capabilities.

For cash-strapped public hospitals grappling with radiologist shortages, AI brings timely interventions at lower costs as compared to hiring additional specialists. AI eliminates the need or delays in seeking expert opinion from other facilities or cities. Even large private healthcare networks are recognizing AI as strategic necessity rather than just an option to boost their brand differentiation.

Government policies play a catalytic role in wider deployment. Regulatory bodies in some countries are promoting standardized AI frameworks, validation processes and data sharing to facilitate integrated hospital rollouts. Vendors are heavily investing in developing versatile AI platforms that can scale across departments from radiology to cardiology to pathology. Cloud-based delivery models are also gaining acceptance, making even small clinics capable of accessing sophisticated AI technologies on-demand as services.

For instance, in March 2024, Philips and Synthetic MR announced collaboration in the field of medical diagnostics by launching an AI-powered quantitative brain imaging system. This innovative technology, called Smart Quant Neuro 3D, aims to revolutionize the diagnosis and analysis of neurological disorders, including dementia, traumatic brain injuries (TBI), and multiple sclerosis (MS).