Description

The global AI-enabled 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 speech recognition to self-driving cars, AI has made its way into daily lives and various industries, including healthcare. AI has become a critical component in the healthcare business, from medication discovery and development to image-guided therapy. Artificial intelligence (AI) algorithms, particularly 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 broad array of applications, propelling it forward at a rapid pace.

Rising healthcare costs have aided the integration of AI in healthcare, a lack of communication between physicians and patients, poor health conditions, a shortage of physicians and medical staff, and the rising prevalence of chronic health disorders. As a result, the market's leading manufacturers have created AI-based tools and methodologies for simulating human cognitive activities and analyzing complex medical data in healthcare settings.

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, and predictive analysis and risk assessment, among others. The AI algorithms identify patterns in medical images after being trained using many examinations and images, thus detecting abnormalities. Furthermore, deep learning algorithms are used for high-throughput extraction of quantitative data and peculiar features from the images. Likewise, the machine learning algorithms provide valuable information for predicting treatment response and the differentiation of benign and malignant tumors.

COVID-19 Impact on the Global AI-Enabled X-Ray Imaging Solutions Market

Immediately after the outbreak of the COVID-19 pandemic, the focus of the healthcare systems switched to managing the pandemic and related crisis. This led to hospital budgets shrinking and thus resulting in the grim growth of AI.

However, AI is being deployed in radiology departments across the globe to help fight the COVID-19 pandemic. AI-based tools are playing an important role in the pandemic. In China, for instance, an AI model has been deployed at 34 hospitals across the country. The model detects chest CT scans suspicious for COVID-19 patients to be isolated and tested. Similarly, in the U.K, Mexico, and Italy, based on the chest X-Rays pattern and opacities, AI is used to classify low, medium, or high-risk COVID-19 patients. Another algorithm monitors the progression of lung disease on the chest X-Rays of ICU patients. In addition, many AI-based companies are allowing hospitals to use services and technology free of cost or on a trial basis for research that is beneficial to both patients and companies. For instance, Mount Sinai Hospital in New York City is studying the potential of AI to detect COVID-19 by evaluating imaging findings along with the clinical history of patients and demographic characteristics. Thus, research studies suggest that radiologists have played an important role in identifying suspected COVID-19 patients and their disease progression.

Global AI-Enabled X-Ray Imaging Solutions Market Dynamics

Drivers: Expanding Range of Applications

With the ongoing advancements in healthcare information technology, the scope of application of AI-enabled medical imaging is rapidly expanding. The use of AI-enabled medical imaging solutions is not limited to cancer screening. It is also becoming widespread in the fields such as neurodiagnostic, coronary diagnostics, and other general medical imaging procedures.

Moreover, algorithms based on AI 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 instance, the researchers at the Massachusetts General Hospital have introduced an algorithm for automatically labeling the vertebral column and grading spinal stenosis, for which MRI is the most frequently used diagnostic tool. MRI examinations are costly, have high inter-reader variability, and have lengthy acquisition times. Thus, the integration of AI-enabled solutions can assist the radiologists in improving the reporting consistency and decreasing the inter-reader variability.

Restraints: Privacy and Security Concerns Related to Healthcare Data

By deciphering medical device images, expediting medical research, and suggesting diagnoses, AI in healthcare focuses on evaluating patient data to enhance results. A considerable amount of health data is required to train a specific algorithm or AI model. Yet, strict privacy and security concerns constitute a substantial barrier to using this data in developing AI models.

Under federal law, patient data is heavily safeguarded, and any failure or breach in maintaining its integrity could result in legal and financial penalties. The majority of countries have enacted strong privacy laws and regulations that must be obeyed to obtain patient information. For example, the Health Insurance Portability and Accountability Act (HIPAA) is a policy in the U.S. that ensures patient privacy while still requiring the patient's agreement to disclose 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 also establishing decision-making patterns. In the early 2000s, the discovery of artificial neural networks (ANNs) led to deep learning technologies. With multilayers of neurons, ANNs are evolving and getting more powerful, sophisticated, and deeper, allowing deep learning to assist strong machine learning.

Deep learning is a subtype of machine learning in AI that mimics the human brain and processes data while also establishing decision-making patterns. In the early 2000s, the discovery of artificial neural networks (ANNs) led to deep learning technologies. With multilayers of neurons, ANNs are evolving and getting more powerful, sophisticated, and deeper, allowing deep learning to assist strong machine learning.

Scope of the AI-Enabled X-Ray Imaging Solutions

The study categorizes the AI-enabled X-Ray imaging solutions market based on product, workflow, mode of deployment, and therapeutic application at the regional and global levels.

By Product Type Outlook (Sales/Revenue, USD Million, 2017-2030)

• Hardware
• Software

By Work Flow Outlook (Sales/Revenue, USD Million, 2017-2030)

• Image Acquisition
• Image Analysis
• Detection
• Diagnosis and Treatment Decision Support
• Predictive Analysis and Risk Assessment
• Triage
• Reporting and Communications

By Mode of Deployment Outlook (Sales/Revenue, USD Million, 2017-2030)

• Cloud and Web-Based Solutions
• On-Premises Solutions

By Therapeutic Application Outlook (Sales/Revenue, USD Million, 2017-2030)

• General Imaging
• Specialty Imaging

By Region Outlook (Sales/Revenue, USD Million, 2017-2030)

• North America (US, 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, the Netherlands, Norway, Sweden, Denmark, Rest of Europe)
• Asia Pacific (China, Japan, India, South Korea, Indonesia, Malaysia, Thailand, Vietnam, Myanmar, Cambodia, the Philippines, Singapore, Australia & New Zealand, Rest of Asia Pacific)
• The Middle East & Africa (Saudi Arabia, UAE, South Africa, Northern Africa, Rest of MEA)

The software segment is projected to account for the largest market share, by product type

The market has been broadly segmented based on the type of products, including hardware and software. Software is the dominating contributor in the market, with a market share of 75.8% in 2021. The software segment includes the machine learning and deep learning solutions used in medical imaging. After being trained by using numerous examinations and images, the AI software solutions are used for various applications, including identification of image patterns and anatomical markers, improvement of radiology workflow, image analysis and acquisition, decision support, treatment selection, and monitoring, predictive analysis, and reporting and communication, among others.

Presently, the market is witnessing an exponential increase in the number of investments and funding to develop AI-based solutions for use in medical imaging. Due to AI technology's promising potential, numerous investors are providing funds to the software manufacturers, which is, in turn, fuelling the market growth. Additionally, the expected emergence of several other companies with AI-based medical imaging solutions under late stages of development is also expected to propel the market growth.

Asia Pacific accounts for the highest CAGR during the forecast period

Based on the regions, the global AI-enabled X-Ray imaging solutions market has been segmented across North America, Asia-Pacific, Europe, South America, and the Middle East & Africa. The Asia-Pacific region is expected to witness the highest CAGR of 22.9% during the forecast period 2022-2030. Most of the countries in the Asia-Pacific region are emerging economies facing significant technological advancements and improvements in 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. Thus, the integration of AI in radiology practice is a crucial requirement. The manufacturers, along with the government and non-government organizations, are promoting AI in medical imaging.

Key Market Players

Every company follows its business strategy to attain the maximum market share. Currently, Agfa-Gevaert NV, Behold.AI Technologies Limited, Carestream Health, Inc., Arterys, Inc., Enlitic, Inc., General Electric Company, Infervision Medical Technology Co. Ltd., Konica Minolta, Inc., Lunit, Inc., Imagen Technologies, Inc., Quibim S.L., Siemens Healthineers AG, Vuno Co. Ltd., Qure.AI Technologies Pvt. Ltd., and Zebra Medical Vision, Inc. are some of the leading players operating in the global AI-enabled X-Ray imaging solutions market.

Recent Developments

• In June 2021, Overjet, Inc. acquired American Dental Examiners (ADE). With this acquisition, Overjet was the first company in the dental industry that offers under one roof a fully integrated solution for dental payers that combines AI and dental claims reviewers.
• In May 2021, Qure.ai Technologies partnered with Fujifilm Holdings Corporation to introduce X-Ray solutions augmented with Qure.ai’s qXR; a computer-aided radiology software application.
• In August 2021, Fujifilm Holdings Corporation received Japan's Pharmaceuticals and Medical Devices Agency approval for its CXR-AID, an AI chest X-Ray analysis system developed in collaboration with Lunit, Inc.

Key Issues Addressed

• What is the market size by various segmentation of the AI-enabled X-Ray imaging solutions by region and its respective countries?
• What are the customer buying behavior, key takeaways, and Porter's five forces of the AI-enabled X-Ray imaging solutions market?
• What are the key opportunities and trends for manufacturers in the AI-enabled X-Ray imaging solutions supply chain?
• What are the market's fundamental dynamics (drivers, restraints, opportunities, and challenges)?
• What and how are regulations, schemes, patents, and policies impacting the market's growth?
• What are the upcoming technological solutions influencing market trends? How will existing companies adapt to the new change in technology?
• The market player positioning, top winning strategies by years, company product developments, and launches will be?
• How has COVID-19 impacted the demand and sales of AI-enabled X-Ray imaging solutions in the global market? Also, the expected BPS drop or rise count of the market and market predicted recovery period.
• Detailed analysis of the competitors and their latest launch, and what are the prominent startups introduced in the target market? Also, detailed company profiling of 25+ leading and prominent companies in the market.