AI IN MEDICAL IMAGING MARKET SIZE AND SHARE ANALYSIS - GROWTH TRENDS AND FORECASTS (2024-2031)
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).
Key Takeaways from Analyst:
Global AI in medical imaging market growth is driven by increasing investments from healthcare organizations and diagnostic centers to incorporate AI capabilities in medical imaging. AI help radiologists and clinicians to enhance productivity and efficiency by automating routine tasks. North America currently dominates the market due to extensive R&D and adoption of advanced technologies. However, Asia Pacific is expected to witness fastest growth as countries like China and India are witnessing increased healthcare spending and focuses on reducing diagnostic errors.
The ability of AI to detect diseases at an early stage from medical images can offer market growth opportunities. This can significantly improve patient outcomes. Integration of AI with picture archiving and communication systems (PACS) provides opportunity to analyze huge volumes of past patient images. Collaboration of AI vendors with OEMs producing medical imaging equipment can further accelerate adoption. However, data privacy laws can hamper the market growth as lack of standardized regulations can restrict development of large clinical data sets required for deep learning. Reluctance to adopt new technologies and fear of job disruptions among radiologists can also hamper the market growth.
Market Challenge - Lack of skilled AI workforce
Lack of skilled AI professionals can hamper the global AI in medical imaging market growth. While AI is becoming integral for advanced medical imaging technologies, there has been severe shortage of data scientists, machine learning engineers and AI application experts who have in-depth understanding of both technology and medical domain. Training existing workforce on new AI tools and techniques requires significant investment of time and resources. Moreover, attracting new talent is also difficult due to high competition from technology companies. This talent crunch limits the potential of organizations to develop and deploy cutting-edge AI powered medical imaging solutions. Effectively addressing the skills gap requires collaborative efforts from educational institutions, governments, private firms to develop training programs that can bridge the divide between technology and healthcare professionals. Until more skilled AI workforce is available, many life-saving applications of AI may not be realized.
Market Opportunity: Scope for AI in drug discovery and personalized medicine
AI has huge potential in accelerating drug discovery process and enabling personalized healthcare through medical imaging. AI algorithms can analyze huge volumes of medical images, clinical trials data and research literature to better understand disease pathology, identify new drug targets and biomarker. This helps researchers to design and test new drug compounds more efficiently. With the help of patient's medical images and genetic profile, AI can predict best treatment options and generate customized treatment plans for individuals. It also helps in close monitoring of drug efficacy on personalized level. As disease detection and treatment becomes more specific to each patient's needs, AI play a vital role in growth of personalized medicine. With more investment in developing advanced AI applications, the future of healthcare is promising with possibilities of delivering right treatment to right patient at right time.
By Imaging Modality - CT imaging dominates due to its improved diagnostic accuracy
In terms of imaging modality, computed tomography (CT) segment is estimated to contribute the highest market share of 40.1% in 2024, owing to its widespread adoption across major healthcare facilities globally. CT imaging has gained popularity among radiologists and clinicians due to the advantages it provides in diagnostic workflows. The integration of AI allows CT to take these benefits to the next level by improving accuracy of imaging analysis and reducing diagnostic errors.
AI algorithms applied to CT scans are able to detect subtle anomalies and abnormalities that may be overlooked by human readers. Conditions like pulmonary embolism, acute abdominal symptoms, and traumatic brain injuries can be identified more reliably through AI-enhanced CT analysis. This delivers faster diagnosis and treatment initiation for serious illnesses. AI also assist in automated segmentation of CT scans, highlighting regions of interest to radiologists for focused evaluation. This streamline reading workflows and minimizes diagnostic variability between readers.
The drive for precision and personalized medicine is can boost CT usage. Through multi-planar reconstruction and 3D modeling capabilities, CT combined with AI delivers highly detailed anatomic information. This supports sophisticated treatment planning for complex procedures like tumor resections, joint replacements, and interventional radiology interventions. AI further aids treatment response monitoring by facilitating longitudinal studies on CT scans to track therapy outcomes over time.
Growing affordability of CT systems alongside AI-readiness also promotes broader access and market uptake. Vendors are integrating AI into new CT platforms, preempting additional IT integration costs. AI-powered teleradiology solutions additionally facilitate remote reading of CT scans from low resource areas. Such developments address major healthcare access gaps, further cementing CT's position as the standard of care imaging modality.
By Application- Radiology segment dominates due to diverse AI applications
In terms of application, radiology segment is estimated to contribute the highest market share of 33.2% in 2024, due the expansive role AI play across the radiology speciality. From routine diagnostic imaging to complex subspecialty procedures, AI is augmenting radiology workflows in diverse ways.
One of the primary applications include automating routine image reads for common indications like chest x-rays. AI excels at standardized pattern recognition tasks and can rapidly triage unremarkable exams, freeing up radiologists for complicated studies. Natural language processing based AI is also automating report generation for basic exams. This allows for around-the-clock preliminary reporting and faster clinical workflow.
For complicated subspecialty imaging, AI is invaluable through capabilities like automated segmentation. In body MRI, AI enables intelligent segmentation of abdominal and pelvic organs which is incredibly time-consuming manually. This facilitates advanced radiomics for improved cancer staging and treatment response metrics. In neuroimaging, AI driven segmentation aids pre-surgical planning for complex tumors or aneurysms by automatically identifying at-risk structures.
AI additionally enhances radiology education and research activities. Tools that perform real-time image augmentation during reads help impart nuanced anatomical and pathological insights to trainees. For research, AI powered radiogenomics and radiomics platforms assist in automated data extraction from imaging archives to enable large scale multicentric studies at a scale impossible manually.
These diverse use cases have firmly established AI as an indispensable part of modern radiology practice. Vast imaging data repositories combined with complex patient cohorts make radiology particularly amenable for continual AI advancement.
By Deployment - Cloud based deployment ushers in an era of accessibility
In terms of deployment, cloud-based segment is estimated to contribute the highest market share of 43.2% in 2024, owing to the accessibility and ease of use it provides to both clients and platform vendors. For healthcare organizations, transitioning analytics capabilities to the cloud alleviates expensive on-premise infrastructure maintenance and software license costs. This makes AI adoption more feasible even for cash-strapped public facilities and small private practices.
From the vendor side, cloud hosting allows for seamless software updates, scaled performance, and centralized data management. AI models trained on aggregate imaging data from multiple client sites cannot be feasibly implemented without cloud technology. This accelerates AI innovation through real-world evidence generation. Platforms can also introduce new applications via software-as-a-service partnerships without clients shouldering additional hardware investments.
For clinicians, cloud deployment puts advanced AI functionalities within easy reach through web and mobile apps. This unprecedented accessibility amplifies AI's clinical impact potential through improved diagnostic consistency worldwide. Even facilities with limited local IT support can access sophisticated AI-driven speciality reads. AI also enables on-demand retrospective data reviews and consultations that overcome physical and temporal barriers between specialities.
Patients benefit from cloud AI achieving universal healthcare coverage globally. Life-saving diagnostics become available regardless of location or infrastructure. This paradigm shifting accessibility paradigm crowns Cloud deployment as the premier enabler of AI transformation within medical imaging.
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North America has established itself as the dominant region for AI in medical imaging market with an estimated market share of 40.3% in 2024, due to region's strong economic conditions and high healthcare expenditure that enables widespread adoption of new medical technologies. The U.S. has a large number of leading AI companies and startups focusing on medical imaging applications. For example, several major tech giants like IBM, Microsoft and Intel have made sizeable investments in developing AI-powered imaging solutions.
The region also has a supportive regulatory environment that encourages innovation. The U.S. FDA has streamlined its clearance process for certain AI medical devices to help new products to market faster. This provides incentives for local businesses to develop AI imaging tools. North American hospitals and healthcare providers are increasingly open to integrating such advanced technologies into their clinical workflows. This early integration helps build experience that further drives the development and refinement of AI imaging tools.
Asia Pacific has emerged as the fastest growing regional market for AI in medical imaging. China is accelerating at a rapid pace due to strong government support for the healthcare AI sector. The Chinese government has identified medical AI as a strategic priority and offers funding and tax incentives to develop domestic expertise and commercialize new products. This is reflected in rising number of Chinese AI companies entering the medical imaging space. Large patient population and growing medical infrastructure spending creates a massive potential market for AI tools.
Other Asian countries like Japan, South Korea and India are also contributing to the regional growth. For example, both Japan and South Korea have universal healthcare systems and a demand for solutions that help overcome challenges like physician shortages in rural areas. This has prompted aggressive funding of AI initiatives by public and private entities. Significant investments are being made in areas such as radiology, pathology and ophthalmology. The region's strong IT expertise and low manufacturing costs further enhance its competitiveness in supplying the global AI in medical imaging market.
AI in Medical Imaging Market Report Coverage
| Report Coverage | Details | ||
|---|---|---|---|
| Base Year: | 2023 | Market Size in 2024: | US$ 1.21 Bn |
| Historical Data for: | 2019 to 2023 | Forecast Period: | 2024 to 2031 |
| Forecast Period 2024 to 2031 CAGR: | 34.4% | 2031 Value Projection: | US$ 9.60 Bn |
| Geographies covered: |
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| Segments covered: |
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| Companies covered: |
GE Healthcare, Siemens Healthineers, Canon Medical Systems, Philips, Aidoc, Fujifilm Holdings Corporation, Imagia Cybernetics, Lunit, Enlitic, iCAD Inc., ContextVision, Subtle Medical, CancerCenter.ai, Viz.ai, Zebra Medical Vision, Qure.ai, Zebra Medical Vision, PathAI, Tempus, Dascena |
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*Definition: Global AI in medical imaging market refers to the incorporation of artificial intelligence capabilities into medical imaging devices, software and procedures. It allows the development of algorithms that can analyse medical images like X-rays, CT scans, MRI scans and ultrasound scans to detect diseases more accurately. AI technologies are helping radiologists and doctors spend less time on administrative tasks and more time on diagnosis and treatment, improving healthcare outcomes.
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About Author
Komal Dighe
Komal Dighe is a Management Consultant with over 8 years of experience in market research and consulting. She excels in managing and delivering high-quality insights and solutions in Health-tech Consulting reports. Her expertise encompasses conducting both primary and secondary research, effectively addressing client requirements, and excelling in market estimation and forecast. Her comprehensive approach ensures that clients receive thorough and accurate analyses, enabling them to make informed decisions and capitalize on market opportunities.
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