AI-Enabled X-ray Imaging Solutions Market Report Covers Future Trends with Research from 2022 to 2030

Global AI-based X-ray Imaging Solutions Market is expected to grow from USD 101.6 Million in 2021 to USD 569.6 Million by 2030, at a CAGR of 20.2% from 2022 to 2030. Artificial intelligence (AI) is currently evolving rapidly, given the availability of huge amounts of data and better machine learning algorithms. From voice recognition to self-driving cars, AI has made its way into daily life and various industries, including healthcare. AI has become an essential part of the healthcare industry, from drug discovery and development to image-guided therapy. Artificial intelligence (AI) algorithms, especially deep learning, have made significant progress in image recognition tasks. In the field of medical image analysis, methods ranging from convolutional neural networks to variational autoencoders have found a wide range of applications, propelling it forward at a rapid pace.

Rising healthcare costs have fostered the integration of AI into healthcare, a lack of communication between doctors and patients, poor health conditions, a shortage of doctors and medical staff, and the increasing prevalence of chronic health conditions. As a result, leading manufacturers in the market have created AI-based tools and methodologies to simulate human cognitive activities and analyze complex medical data in healthcare facilities.

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In the field of medical imaging, AI-based X-ray solutions are used for image analysis, detection, diagnosis and decision support, image acquisition, reporting and communication, triage, equipment maintenance, predictive analytics and risk assessment, among others. AI algorithms identify patterns in medical images after being trained using numerous exams and images, thereby detecting abnormalities. Additionally, deep learning algorithms are used for high-throughput extraction of quantitative data and particular features from images. Likewise, machine learning algorithms provide valuable information to predict treatment response and differentiation of benign and malignant tumors.

Impact of COVID-19 on the Global AI-Enabled X-ray Imaging Solutions Market
Immediately after the outbreak of the COVID-19 pandemic, health systems focused on managing the pandemic and related crisis. This has led to shrinking hospital budgets and thus led to grim growth in AI.

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However, AI is being deployed in radiology departments around the world to help fight the COVID-19 pandemic. AI-based tools play an important role in the pandemic. In China, for example, an AI model has been deployed in 34 hospitals across the country. The model detects suspicious chest CT scans for COVID-19 patients to isolate and test. Similarly, in the UK, Mexico and Italy, based on the pattern of chest X-rays and opacities, AI is used to categorize COVID-19 patients as low, medium or high risk. Another algorithm monitors the progression of lung disease on chest X-rays of patients in intensive care. Additionally, many AI-based companies allow hospitals to use services and technologies for free or on a trial basis for research that benefits both patients and businesses. For example, Mount Sinai Hospital in New York is investigating the potential of AI to detect COVID-19 by evaluating imaging results as well as patient clinical histories and demographic characteristics. Thus, research studies suggest that radiologists have played an important role in identifying patients with suspected COVID-19 and in the progression of their disease.

Global AI-Enabled X-ray Imaging Solutions Market Dynamics
Drivers: extension of the range of applications
With the continuous advancements in healthcare information technology, the scope of AI-based medical imaging is rapidly expanding. The use of AI-based medical imaging solutions is not limited to cancer screening. It is also spreading in areas such as neurodiagnostics, coronary diagnostics, and other general medical imaging procedures.

Additionally, AI-based algorithms are currently being used to detect critical bone disorders such as spinal stenosis. They are even used for the diagnosis and prevention of childhood blindness. For example, researchers at Massachusetts General Hospital introduced an algorithm to automatically label the spine and classify spinal stenosis, for which MRI is the most frequently used diagnostic tool. MRI examinations are expensive, have high inter-reader variability and long acquisition times. Thus, the integration of AI-based solutions can help radiologists improve reporting consistency and reduce inter-reader variability.

Constraints: confidentiality and security issues related to health data
By deciphering images of medical devices, accelerating medical research and suggesting diagnoses, AI in healthcare is focused on evaluating patient data to improve outcomes. A considerable amount of health data is needed to train a specific algorithm or AI

model. Yet strict privacy and security concerns are a significant barrier to using this data in the development of AI models.

Under federal law, patient data is strongly protected and any failure or violation in maintaining its integrity could result in legal and financial penalties. The majority of countries have strict privacy laws and regulations that must be followed when obtaining patient information. For example, the Health Insurance Portability and Accountability Act (HIPAA) is a policy in the United States that guarantees patient confidentiality while requiring patient consent to release information.

Opportunities: Rapidly evolving machine and deep learning techniques
Deep learning is a subtype of machine learning in AI that mimics the human brain and processes data while building models for decision making. In the early 2000s, the discovery of artificial neural networks (ANN) led to deep learning technologies. With neural multilayers, ANNs evolve and become more powerful, sophisticated, and deeper, enabling deep learning to aid powerful machine learning.

Deep learning is a subtype of machine learning in AI that mimics the human brain and processes data while building models for decision making. In the early 2000s, the discovery of artificial neural networks (ANN) led to deep learning technologies. With neural multilayers, ANNs evolve and become more powerful, sophisticated, and deeper, enabling deep learning to aid powerful machine learning.

Scope of AI-Enabled X-ray Imaging Solutions
The study categorizes the AI-based X-ray imaging solutions market based on product, workflow, mode of deployment, and therapeutic application regionally and globally.

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Outlook by Product Type (Sales/Revenue, USD Million, 2017-2030)
By Workflow Outlook (Sales/Revenue, USD Million, 2017-2030)
Image capture
Image analysis
Diagnosis and treatment decision support
Predictive analysis and risk assessment
Reports and communications
By Outlook Deployment Mode (Sales/Revenue, USD Million, 2017-2030)
Cloud and web solutions
On-site solutions
By Therapeutic Application Outlook (Sales/Revenue, USD Million, 2017 – 2030)
General imaging
Specialized imaging
Outlook by Region (Sales/Revenue, USD Million, 2017-2030)
North America (United States, Canada, Mexico)
South America (Brazil, Argentina, Colombia, Peru, Rest of Latin America)
Europe (Germany, Italy, France, UK, Spain, Poland, Russia, Slovenia, Slovakia, Hungary, Czech Republic, Belgium, Netherlands, Norway, Sweden, Denmark, Rest of Europe)
Asia-Pacific (China, Japan, India, South Korea, Indonesia, Malaysia, Thailand, Vietnam, Myanmar, Cambodia, Philippines, Singapore, Australia and New Zealand, Rest of Asia-Pacific)
The Middle East and Africa (Saudi Arabia, United Arab Emirates, South Africa, North Africa, Rest of MEA)
Software segment is expected to account for the largest market share, by product type
The market has been broadly segmented on the basis of product type including hardware and software. Software is the dominant contributor to the market, with a market share of 75.8% in 2021. The software segment includes machine learning and deep learning solutions used in medical imaging. After being trained using numerous exams and images, AI software solutions are used for various applications, including identifying image patterns and anatomical markers, improving radiology workflow, image analysis and acquisition, decision support, treatment selection and monitoring, predictive analysis, reporting and communication, among others.

Currently, the market is witnessing an exponential increase in the number of investments and funding to develop AI-based solutions for medical imaging. Due to the promising potential of AI technology, many investors are providing funds to software makers which, in turn, is fueling the growth of the market. Furthermore, the expected emergence of several other companies offering AI-based medical imaging solutions at advanced stages of development is also expected to propel the market growth.

Asia-Pacific accounts for the highest CAGR during the forecast period

Based on regions, the global AI-enabled X-ray imaging solutions market has been segmented into North America, Asia-Pacific, Europe, South America, and Middle East & Africa. The Asia-Pacific region is expected to witness the highest CAGR of 22.9% during the forecast period 2022-2030. Most countries in the Asia-Pacific region are emerging economies facing significant technological advances and improvements in healthcare systems.

Moreover, since the region comprises more than half of the world’s population, the burden of health care is increased, which necessitates proper diagnosis of the disease. However, there is a lack of proper diagnosis in the region attributed to lack of proper infrastructure and low radiologist-to-patient ratio. For

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AI-Enabled X-ray Imaging Solutions Market Report Covers Future Trends with Research from 2022 to 2030

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