AI in Medical Imaging Market

AI in Medical Imaging Market Overview

The AI in medical imaging market size is projected to grow from $1.75 billion in 2024 to $8.56 billion by 2030, growing at a CAGR of 30% during the forecast period from 2024 to 2030.

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The new research study consists of pipeline analysis, partnerships and collaborations, funding and investments analysis, company valuation analysis, patent analysis, cost saving analysis and detailed market analysis. The AI in medical imaging market growth over the next decade is likely to be the result of increasing adoption of artificial intelligence (AI) technology, particularly in deep learning algorithms, increasing shift toward personalized and precision medicine, unmet needs amongst the target population and support from the venture funds.

Deep learning is a machine learning approach that involves the use of intuitive algorithms and artificial neural networks to facilitate unsupervised pattern recognition / insight generation from large volumes of unstructured data. This technology is gradually being incorporated in a variety of applications across the healthcare sector, including imaging-based medical diagnosis and data processing.

Specifically concerning medical imaging, deep learning has the potential to be used to automate information processing and result interpretation for a variety of diagnostic images, such as X-rays, computed tomography scans, magnetic resonance imaging, and positron emission tomography. In this context, it is worth mentioning that the manual examination of medical images is limited, both in terms of accuracy (resulting in misdiagnosis) and throughput (leading to delays in communication of results). As a result, in situations characterized by low physician / pathologist to patient ratios, the conventional mode of operation is rendered inadequate. Experts have predicted a shortage of 10,000 to 40,000 physicians, by 2030, in the US alone.

Over time, various industry stakeholders have designed proprietary deep learning algorithms for processing of medical images. Presently, many innovators claim to have developed the means to train computers to read and triage medical images, and recognize patterns related to both temporal and spatial changes (which are not even visible to the naked eye). Experts in the field of artificial intelligence diagnosis also believe that the use of deep learning can actually speed up the processing and interpretation of radiology data by 20%, reducing the rate of false positives by approximately 10%.

This upcoming segment of the pharmaceutical industry that exists at the interface between medicine and information technology has garnered the attention of prominent venture capital firms and strategic investors. In the long term, the AI in medical imaging market is anticipated to witness significant market growth as more machine learning based solutions are approved for use, during the forecast period.

AI In Medical Imaging Market Report Coverage

The AI in medical imaging market research report presents an in-depth analysis of the various companies that are engaged in AI in medical imaging industry, across different segments, as defined in the table below:

Global AI in Medical Imaging Market: Report Attributes / Market Segmentations

Key Report Attribute	Details
Historical Trend	2020-2022
Base Year	2023
Forecast Period	2024-2030
Market Size 2030	$ 8.56 Billion
CAGR	30%
Application Area	Lung Infections / Respiratory Disorders Brain Injuries / Disorders Lung Cancer Cardiac Conditions / Cardiovascular Disorders  Bone Deformities / Orthopedic Disorders Breast Cancer  Others Application Areas
Type of Image Processed	X-ray  MRI  CT Ultrasound
Key Geographical Regions	North America Europe Asia Pacific and Rest of the World
Key Companies Profiled	Artelus Arterys Butterfly Network ContextVision Enlitic Echonous GE Healthcare InferVision VUNO  (Full list of 495+ companies captured is available in the report)
PowerPoint Presentation (Complimentary)	Available
Customization Scope	15% Free Customization
Excel Data Packs (Complimentary)	Market Landscape Analysis Partnership and Collaboration Analysis Funding and Investment Analysis Clinical Trial Analysis Patents Analysis Cost Saving Analysis Market Forecast and Opportunity Analysis

One of the key objectives of this AI in medical imaging market report was to estimate the current market size, opportunity and the future market growth potential over the forecast period. Based on global radiology spending across countries, number of radiologists employed across different regions of globe, annual salary of radiologists, rate of adoption of deep learning-based solutions, we have provided an informed estimate on the market evolution during the forecast period 2024-2030.

The market report also features the likely distribution of the current and forecasted opportunity within the AI in medical imaging market across various segments, such as application area (lung infections / respiratory disorders, brain injuries / disorders, lung cancer, cardiac conditions / cardiovascular disorders, bone deformities / orthopedic disorders, breast cancer and others), type of image processed (X-ray, MRI, CT, ultrasound) and key geographical regions (North America, Europe, Asia Pacific and rest of the world).  In order to account for future uncertainties and to add robustness to our model, we have provided three market forecast scenarios, namely conservative, base and optimistic scenarios, representing different tracks of the AI in medical imaging market growth.

The opinions and insights presented in the market research report were also influenced by discussions held with multiple stakeholders in AI in medical imaging market. The market report features detailed transcripts of interviews held with the following individuals:

• Research Scientist, ContextVision
• CXO, AlgoSurg
• Senior Director of Product, Arterys
• Head of Strategy and Marketing, Arterys
• CXO, Advenio Technosys
• CXO, Arya.ai
• CXO, Arya.ai

All actual figures have been sourced and analyzed from publicly available information forums and primary research discussions. Financial figures mentioned in this report are in USD, unless otherwise specified.

AI In Medical Imaging Market: Key Insights

The “AI in Medical Imaging Market: Industry Trends and Global Forecasts, till 2030” market report features an extensive study of the current market landscape, market size, market share, market analysis, market forecast and future opportunities for the AI in medical imaging service provides involved in the pharmaceutical market. The report highlights the efforts of several companies engaged in this rapidly emerging market segment of the diagnostics industry. Key takeaways of the AI in medical imaging market analysis are briefly discussed below.

Current AI in Medical Imaging Market Landscape

The current market landscape features the presence of close to 70 AI in medical imaging companies, spread across the globe. Overall, the market seems to be well-fragmented, featuring the presence of large, mid-sized and small companies, which offer deep learning solutions for the assessment of chest region, including lungs, heart, and rib cage. Further, most of the solutions (42%) are being used for analyzing CT images, followed by those employed for processing MRI (24%), X-ray (21%) and ultrasound images (16%).

AI in Medical Imaging Market Trends: Partnerships and Collaborations on the Rise

In recent years, several partnerships have been established by industry stakeholders, in order to enhance their capabilities and consolidate their presence within the AI in medical imaging market. In December 2023, Enlitic entered into an agreement with INFINITT for integration of Enlitic’s data standardization solution ENDEX™ with INFINITT PACS. This agreement is expected to ensure standardized medical imaging and enable advanced data interoperability for the end-users. In October 2023, Aidoc announced the expansion of its partnership with GLEAMER in order to enhance integrate GLEAMER’s ChestView AI solution for chest imaging across computed tomography and X-ray.

It is also worth mentioning that in the past few years, the FDA has provided the necessary clearances and approved the use for a variety of deep learning or AI imaging software. Moreover, several technology-focused innovators, such as (in alphabetical order) IBM, GE Healthcare and Google, have entered into strategic alliances with big pharma players, in order to bring proprietary deep learning-based medical solutions to the market.

Market Drivers and Restraints: Burden of Image Processing to Drive the AI in Medical Imaging Market Growth

It is estimated that 90% of medical data generated in hospitals is in the form of images; this puts an immense burden on radiologists and other consulting physicians related to processing such large volumes of data. In fact, according to a study published in the American Journal of Medicine, ~15% of reported medical cases in developed countries, are misdiagnosed. In addition, close to 1.5 million individuals are estimated to die each year, across the world, due to misdiagnosis. On the other hand, accurate artificial intelligence diagnosis at an early stage has been demonstrated to allow significant cost savings for both patients and healthcare providers. In this scenario, deep learning and other artificial intelligence imaging technologies are currently being developed / investigated to automate such processes.

AI in Medical Imaging Market Size Analysis: Diagnosis for Treatment of Brain Abnormalities / Neurological Disorders to Hold Majority Market Share

The global AI in medical imaging market size is estimated to reach USD 8.56 billion by 2030. The market growth is expected to be driven by the increasing adoption of AI in healthcare and rising demand of precision medicine, leading to a CAGR of over 30% during the forecast period. Further, in terms of application area, the treatment for brain abnormalities / neurological disorders segment are envisaged to capture majority of the current and future market share.

Developments are also taking place for cardiovascular disorder diagnosis. In June 2023, EchoNous entered into an agreement with UltraSight in order to integrate UltraSight’s real-time AI guidance software with EchoNous’ handheld ultrasound scanner device to enable accurate and precise echocardiographic examinations.

Regional Analysis: North America to Hold the Largest Share in AI in Medical Imaging Market

Majority of the companies offering AI in medical imaging are headquartered in North America, followed by companies based in Europe. Consequently, close to 70% of the global market for AI in medical imaging is anticipated to be captured by companies based in North America, in 2035.

Key Companies Involved in AI in Medical Imaging Market

Examples of key companies integrating AI in medical imaging (which have also been profiled in this market report; the complete list of companies is available in the full report) include Artelus, Arterys, Butterfly Network, ContextVision, Enlitic, Echonous, GE Healthcare, InferVision and VUNO. This market report includes an easily searchable excel database of all the AI in medical imaging companies, worldwide.

AI In Medical Imaging Market Report Coverage

The market report presents an in-depth analysis, highlighting the capabilities of various stakeholders engaged in this industry, across different geographies. Amongst other elements, the market report includes:

• A preface providing an introduction to the full report, AI in Medical Imaging Market: Industry Trends and Global Forecasts, till 2030.
• An outline of the systematic research methodology adopted to conduct the study on AI in medical imaging , providing insights on the various assumptions, methodologies, and quality control measures employed to ensure accuracy and reliability of our findings.
• An executive summary of the key insights captured during our research, offering a high-level view on the current landscape of the AI in medical imaging market and their likely evolution in the short to mid and long term.
• A brief introduction that presents details on the digital revolution in the medical industry. It elaborates on the growth of artificial intelligence and machine learning tools, such as deep learning algorithms, along with a discussion on their potential applications in solving some of the key challenges faced by the healthcare industry. The chapter also gives an overview on the rise of big data and its role in providing personalized and evidence-based care to patients. It emphasizes on the applications of deep learning in healthcare sector with detailed information on areas including personalized medicine and drug discovery, personal fitness and lifestyle management, clinical trial management and medical image processing. Additionally, it includes an analysis of contemporary trends, as observed on Google Trends and insights from the recent news articles related to deep learning and medical image processing, indicating the increasing popularity of this domain.
• A detailed review of the current market landscape of AI based medical imaging solutions for medical image processing, along with information on their status of development (launched / under development), regulatory approvals (FDA, CE mark, others), type of offering (diagnostic software / tool, diagnostic software / tool + device), type of image processed (X-ray, MRI, CT, ultrasound), application area (lung infections / respiratory disorders, brain injuries / disorders, lung cancer, cardiac conditions / cardiovascular disorders, bone deformities / orthopedic disorders, breast cancer and others). In addition, it presents details of companies developing such solutions, such as their year of establishment, company size, location of headquarters and focus area (in terms of type of deployment model). Further, it highlights key features of each solution and affiliated technologies. 
• An in-depth analysis of the contemporary market trends, presented using three schematic representations, including a grid representation illustrating the distribution of solutions based on application area, type of image processed and type of offering and an insightful map representation highlighting the geographical activity of the players. 
• Elaborate profiles of key players that are engaged in the development of deep learning-based solutions intended for processing of medical images. Each company profile features a brief overview of the company (including information on year of establishment, number of employees, location of headquarters and key members of the executive team), details of their respective portfolio of solutions, recent developments and an informed future outlook.
• An analysis of the partnerships that have been inked by stakeholders in AI in medical imaging market, during the time period 2016-2020 (till June), covering research / development agreements, solution utilization agreements, solution integration agreements, marketing / distribution agreements, other relevant types of deals.
• An analysis of the investments made, including seed financing, venture capital financing, debt financing, grants and others, in companies that are focused on developing deep learning-based solutions intended for processing of medical images.
• An elaborate valuation analysis of companies that are involved in applying deep learning in solutions intended for processing of medical images. Further, we have built a multi-variable dependent valuation model to estimate the current valuation of a number of companies engaged in AI in medical imaging market.
• A clinical trial analysis of completed, ongoing and planned studies (available on ct.gov), focused on the assessment deep learning-based software solutions, based on various parameters, such as trial registration year, trial recruitment status, trial design, target therapeutic area, leading industry and non-industry players, and geographical locations of trials. 
• An in-depth analysis of over 3,000 patents related to deep learning and medical images that have been filed / granted till June 2020, highlighting key trends associated with these patents, across type of patent, publication year and application year, regional applicability, CPC symbols, emerging focus areas, leading patent assignees (in terms of number of patents filed / granted), patent benchmarking and valuation.  
• An insightful analysis highlighting cost saving potential associated with the use of deep learning solutions intended for processing of medical images, based on information gathered from close to 30 countries, taking into consideration various parameters, such as total number of radiologists, annual salary of radiologists, number of scans performed (across each type of image) and increase in efficiency by adoption of deep learning solutions. 
• An insightful discussion on the views presented by various industry and non-industry experts presents across the globe, on various portals, such as YouTube and other media platforms. The summary of insights provided by each expert is discussed across focus area, current industry status / challenges and future outlook.

Recent Developments in AI in Medical Imaging Market:

Several recent developments have taken place in the field of AI medical imaging. We have outlined some of these recent initiatives below. These developments, even if they took place post the release of our market report, substantiate the overall market trends that have been outlined in our analysis.

• In March 2026, Harrison.ai received FDA clearance for its acute infarct triage software on non-contrast CT brain scans, identifying and prioritizing suspected ischemic stroke cases across multiple vascular territories.
• In March 2026, Radiology Partners received FDA Breakthrough Device designation for Cognita Chest X-ray using generative vision-language model to interpret chest X-rays and draft radiology reports for physician review across multiple indications.
• In March 2026, RadNet announced acquisition of Gleamer for up to €230 million in order to expand its clinical AI offerings and strengthen European market presence.
• In March 2026, Medimaps and Radiobotics announced a strategic merger. It combines their AI tools for MSK imaging on X-ray and DXA systems into one solution for detecting fractures, assessing bone quality, and predicting risks in musculoskeletal care.

Frequently Asked Questions

Question 1: What is AI in medical imaging?

Answer: AI in medical imaging refers to the use of artificial intelligence and deep learning algorithms to be used to automate information processing and result interpretation for a variety of diagnostic images, such as X-rays, computed tomography scans, magnetic resonance imaging, and positron emission tomography.

Question 2: How big is the AI in medical imaging market?

Answer: The AI in medical imaging industry size is estimated to be worth $1.75 billion in 2024.

Question 3: What is the projected market growth of the AI in medical imaging market?

Answer: The market for AI in medical imaging is expected to grow at compounded annual growth rate (CAGR) of 30% during the forecast period 2024 – 2030.

Question 4: Which type of diagnostic image is primarily processed by the software solutions developed by players?

Answer: Presently, close to 30% of the players engaged in AI in medical imaging market offer software solutions for processing X-ray images.

Question 5: Who are the leading companies in the AI in medical imaging industry?

Answer: : Examples of key companies providing AI in medical imaging services include Artelus, Arterys, Butterfly Network, ContextVision, Enlitic, Echonous, GE Healthcare, InferVision and VUNO. Each of these companies have detailed profiles in our market report; the complete list of companies is available in the full report.

Question 6: How many software solutions are presently available developed by the players engaged in AI in medical imaging industry?

Answer: Over 200 software solutions are presently available developed by the players engaged in AI in medical imaging industry.

Question 7: How much money has been invested by stakeholders engaged in the AI in medical imaging market?

Answer: More than USD 2 billion has been invested by stakeholders engaged in the AI in medical imaging industry on developing deep learning-based software solutions.