The global AI-enabled X-Ray imaging solutions market size was valued at USD 95.35 million in 2022. It is estimated to reach USD 499.41 million by 2031, growing at a CAGR of 18.2% during the forecast period (2023–2031).
Artificial intelligence (AI) is evolving rapidly, given the availability of huge amounts of data and better machine learning algorithms. AI has become a crucial component in the healthcare business, from medication discovery and development to image-guided therapy. Artificial intelligence (AI) algorithms, particularly deep learning, have significantly progressed in image recognition tasks. In medical image analysis, methods ranging from convolutional neural networks to variational autoencoders have found many applications, propelling it forward rapidly.
AI-enabled X-ray imaging solutions are a suite of medical diagnostics that enables radiologists to evaluate and analyze intricate images of various organs and their conditions with increased speed and precision. They use a variety of hardware and software solutions that employ computational algorithms to evaluate data, carry out risk assessments and do predictive analyses. These facilities decrease the need for human specializations, enable information-driven decision-making, improve image quality, lower radiation exposure, and help practitioners provide better clinical care. In order to thoroughly clarify patient anomalies and offer better treatment options, AI-enabled X-ray imaging solutions are widely employed in general and specialty radiology.
Shortage of Healthcare Workforce
Countries across the globe at different levels of socioeconomic development face difficulties in the education, training, deployment, retention, and performance of their health workforce. For instance, according to the WHO, in 2013, a worldwide shortage of 12.9 million is estimated in healthcare workers by 2035. It is not only the developing countries facing the shortage of healthcare workforce; the developed countries such as the U.S. and the U.K. are also witnessing an inequitable healthcare workforce distribution. For instance, according to the European Commission in 2012, the gap in the supply of human resources in healthcare is expected to be approximately 1,000,000 health professionals by 2020.
Furthermore, developing economies such as sub-Saharan Africa and some parts of Asia have inadequate healthcare systems, probably due to the migration of their health professionals. Hence, to address the growing shortage of healthcare workforce, healthcare providers worldwide are increasingly implementing AI-enabled technologies, which mimic cognitive behaviors, thereby combating the lack of a skilled workforce in healthcare. This, in turn, is propelling the expansion of the overall market.
Increasing Funding Activities
Medical imaging is becoming a better, faster, stronger, and more efficient discipline with the incorporation of AI, opening up attractive potential for manufacturers. For instance, deep learning algorithms are known to reduce a 45-minute MRI scan of the brain to only several minutes scan without compromising the quality or accuracy. Attributed to these advantages, investors in the field of medical imaging are leaning toward the development of innovative software solutions which possess the potential to mimic cognitive behaviors, thereby improving the associated workflows.
Additionally, significant research is being conducted to develop novel AI-enabled medical imaging solutions without sufficient consideration of commercialization potential, reimbursement, and other related concerns. This research is being funded by both governments as well as private investors. The market is witnessing a huge number of such investments. The number of products catering to AI-enabled X-Ray imaging solutions is rapidly increasing, thus driving market growth.
Lack of Trained Staff
In radiology, a skilled physician commonly detects, characterizes, and monitors diseases on radiographs. This is much more subjective, based on the technician's and physician's education and experience. It is also not as straightforward as taking a scan and analyzing it in real-time using traditional medical imaging technologies. Additionally, there aren't enough qualified doctors at the scanning site, limiting market expansion.
Increasing Number of Cross-Industry Partnerships
Numerous industry stakeholders have recognized the benefits of using AI in medical imaging. As a result, they are becoming more actively involved in synergistic activities. The well-established players in the information technology industry, including Intel Corporation, IBM, and Microsoft Corporation, are collaborating with several small and large players (for the parent market). For instance, IBM Watson Health and Guerbet entered into a joint development agreement in July 2018 to create an AI-enabled software solution to enhance the detection and treatment of liver cancer using CT and MRI imaging.
Furthermore, the leading providers of medical imaging modalities, including GE Healthcare, Siemens Healthineers AG, and Koninklijke Philips N.V., are also entering into strategic partnerships with the manufacturers of AI-enabled software solutions. For instance, in March 2019, Koninklijke Philips N.V. completed the acquisition of Carestream Health's cloud-based enterprise imaging platform, which is expected to expand its radiology informatics portfolio for imaging data management, workflow enhancement, and advanced visualization and analysis. Such cross-industry partnerships and acquisitions are expected to offer opportunities for market expansion during the forecast period.
The global AI-enabled X-Ray imaging solutions market is bifurcated into product type, workflow, mode of deployment, and therapeutic application.
Based on product type, the global market is divided into hardware and software.
The software segment is the most significant contributor to the market and is estimated to exhibit a CAGR of 21.6% over the forecast period. The software segment includes machine learning and deep learning solutions used in medical imaging. The AI software solutions are used for numerous applications, including identifying image patterns and anatomical markers, enhancing radiology workflow, image analysis and acquisition, decision support, treatment selection and monitoring, predictive analysis, and reporting and communication. They are trained using a large number of exams and images. In addition, owing to the promising potential of AI technology, numerous investors are providing funds to software manufacturers, fuelling market growth. Further, the emergence of several other companies with AI-based medical imaging solutions in the late stages of development is also expected to propel the market growth.
Based on workflow, the global market is divided into image acquisition, detection, diagnosis and treatment decision support, image analysis, predictive analysis and risk assessment, triage, and reporting and communication.
The detection segment accounts for the largest market share and is predicted to exhibit a CAGR of 17.7% over the forecast period. Detection refers to the identification process of a small or a large fraction of data that differs in some sense from the normal physiological trend or pattern. In the current detection workflow, radiologists have to visually scan through stacks of images while periodically adjusting viewing planes and the window width and level settings, relying on perceptive manual skills to identify possible abnormalities, followed by cognitive skills to either confirm or reject the findings. In addition, the most compelling challenge to address is the identification of inefficiencies in radiology workflows. By incorporating AI, radiologists may prioritize their readings and quickly review pictures using automated detection and faster reporting.
Based on the mode of deployment, the global market is bifurcated into cloud-based and web-based solutions and on-premises solutions.
The cloud-based and web-based solutions segment dominates the global market and is predicted to exhibit a CAGR of 20.4% during the forecast period. The on-demand services, computer networks, storage, applications, or resources accessed through the internet and another provider's shared cloud computing infrastructure are called cloud-based solutions. A web-based solution can be accessed through the web browser, and rather than being installed on the desktop, the software and database are accessed over a network. These solutions are relatively easy to develop and provide access and functionality to a broad population. The solutions deployed through these deployment models are cost-effective in development, easily customizable, vendor-neutral for a wide range of devices, and have improved interoperability, installation, and maintenance.
Based on therapeutic application, the global market is bifurcated into general radiology and specialty radiology.
The specialty radiology segment owns the highest market share and is predicted to exhibit a CAGR of 19.7% over the forecast period. This segment includes the dedicated applications of AI-based solutions in specialized disciplines such as the chest, musculoskeletal, fluoroscopy, and others. A deep-learning model was recently discovered to match or outperform human expert radiologists in diagnosing ten or more diseases on chest radiographs. The success of AI in diagnostic imaging has fueled a growing market scenario for AI-enabled X-Ray imaging solutions for chest imaging where deep-learning models can perform important diagnostic tasks autonomously using advanced algorithms.
Imaging is a valuable tool for evaluating patients with musculoskeletal (MSK) problems, and its utility has led to greater use of common MSK imaging modalities. Rising usage has had various downstream impacts on a radiology department or private practice, including a more important requirement to achieve operational efficiency while maintaining good accuracy and imaging report quality. In addition, fluoroscopy is a kind of medical imaging that displays a constant X-Ray image on a monitor, comparable to an X-Ray film. The others segment employs machine learning algorithms that are being employed for use in dentistry and other therapeutic applications.
Based on region, the global AI-enabled X-Ray imaging solutions market is bifurcated into North America, Europe, Asia-Pacific, and the Rest-of-the-World (RoW).
North America is the most significant global AI-enabled X-Ray imaging solutions market shareholder and is estimated to grow at a CAGR of 20.5% over the forecast period. Major factors such as the presence of leading providers of AI solutions and rising adoption of AI-enabled X-Ray imaging solutions are responsible for North America holding the highest share of the market. In the U.S., approximately one-third of hospitals and imaging centers are using artificial intelligence, machine learning, and deep learning in imaging or business operations, according to a survey by Definitive Healthcare. Furthermore, the rising number of strategic collaborations and the expanding scope of application of AI in medical imaging are other major factors contributing to the market growth in North America. For instance, NVIDIA Corporation and the American College of Radiology collaborated to increase the adoption of AI in diagnostic radiology across thousands of hospitals in the region. The collaboration enabled thousands of radiologists throughout the region to use AI for diagnostic radiology in their respective facilities.
Europe is predicted to grow at a CAGR of 18.4% over the forecast period. The rising awareness and the increasing number of government initiatives to expand the use of AI in clinical practice are responsible for the region's share in the global market. In February 2018, the European Society of Radiology and GE Healthcare announced an exclusive partnership for artificial intelligence in ECR 2019. In addition, the European Commission claimed that it invested USD 1.77 billion between 2018-2020 to promote research and innovation in AI. Such investments by government bodies contribute to the region's market growth. Additionally, European Union-funded projects such as the BigMedilytics project aim at bridging the gap between AI and diagnostic imaging. The BigMedilytics project optimizes neoadjuvant breast cancer treatment using AI in diagnostic imaging. Such projects are not just expanding the scope of the application of AI in medical imaging but are also contributing to market growth.
Most of the countries in the Asia-Pacific region are emerging economies that are facing significant technological advancements along with improvements in the healthcare systems. Moreover, since the region comprises more than half of the world's population, there is an increased healthcare burden, making proper disease diagnosis necessary. However, there is a lack of proper diagnosis in the region attributed to the lack of proper infrastructure and the poor radiologist-to-patient ratio. For instance, despite being a populous country, India has approximately one radiologist for every 100,000 population. Similar is the case with China and other Asian countries. Therefore, integrating AI in radiology practice is a crucial requirement attributed to which the manufacturers, along with the government and non-government organizations, are promoting the use of AI in medical imaging.
Countries under Rest-of-the-World (Latin America, Middle East, and Africa) have abundant scope for operational expansion of the global AI-enabled X-Ray and fluoroscopy imaging solutions market. For instance, as per the study conducted by Microsoft Corporation across five countries in the Middle East and Africa region, it was identified that AI is expected to create new business avenues. Additionally, the ongoing investments in developing AI solutions for use in the healthcare industry are fuelling market growth. For instance, Dubai Future Foundation invested substantially in AI development initiatives, including UAE AI and Robotics Award for Good. Likewise, Latin American countries such as Mexico are among the world's top 10 countries to launch strategies for AI.
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