Artificial Intelligence (AI) in Radiology Market - Forecasts from 2024 to 2029

Artificial Intelligence (AI) in Radiology Market - Forecasts from 2024 to 2029


The AI in radiology market is projected to grow at a CAGR of 30.45% during the forecast period, reaching a total market size of US$8,596.802 million by 2029, up from US$2,275.610 million in 2024.

Deep learning algorithms in artificial intelligence (AI) have been perfected in vision-based applications. The medical image analysis domain is expanding rapidly with the realization of variational autoencoders and convolutional neural network techniques. Traditional qualitative assessments for radiographic qualities differ since the measure is easy to perform. Moreover, AI techniques are more advanced at analyzing mechanically difficult information patterns in imaging information. For instance, in radiology, AI algorithms could be designed to measure specific radiographic characteristics, such as the 3D shape of a tumor, every pixel's texture, and pixel intensity within the tumor.

X-ray radiography is when licensed medical doctors study clinical photos and kit a statement or identify and single out diseases that allow disorders to be detected, identified, and monitored. That assessment requires a lot of expertise and experience, which is sometimes susceptible to opinion. In contrast to this qualitative, subjective evaluation, AI is extremely good at identifying subtle patterns in imaging data while automatically providing an objective numerical analysis. Implementing AI to support and assist mammogram physicians can result in more precise and reproducible radiological assessments. This application is opening the door to further development into AI in the radiology market in years or decades to come.

AI IN RADIOLOGY MARKET DRIVERS:

The increasing requirement from the neurological treatment sector is anticipated to drive AI in the radiology market growth.

Working off of volumetric tumor segmentation, AI can improve identification and detection across all brain tumors and other neurological cancers with superior accuracy and consistency. The system will also automatically identify brain tumors on MRI scans. These strategies can be extremely valuable in providing precise diagnoses and assessing the tumor response to treatment in a reproducible and unbiased manner. Another use case in this neurological treatment is the prediction of outcomes using AI, which can assist in utilizing the best strategy. Background Machine learning has been used to predict survival among patients based on blood volume distribution data from MR imaging.

For instance, in February 2023, Avicenna, an AI platform for radiology screening, raised €7 million in a series A venture, bringing its capital base to €10 million. The stage employs image-trained profound learning to recognize and evaluate life-threatening ailments in radiology research facilities. It uses CT scan imaging to prioritize patients with symptoms before diagnosis. Avicenna.AI offers two variations: one for heart attack indications and chance and another for brain damage and stroke chance. The platform assists radiologists in deciding if a patient's life is threatened.

AI In Radiology Market Geographical Outlook:

The Asia Pacific region is expected to hold a substantial AI in radiology market share.

Asia Pacific is projected to hold substantial shares of AI in the radiology market owing to a rise in research spending and development in the medical and biotech industries. Moreover, the expected large number of patients in these areas will increase the demand for better cancer treatment infrastructure, propelling healthcare sector growth and aiding market development at a regional level. Asia Pacific has also seen investment in the healthcare sector, especially in emerging economies, to advance newer technologies.

The region's economies are increasingly focusing on creating a sound healthcare system for early patient diagnosis and treatment. In addition, various key companies are focusing on advancing their reach to Asia Pacific countries by building their facilities, leading to the regional market’s growth in the coming years. For instance, in May 2023, Annalise. ai, an AI-based radiology company that is a global leader in the field, established its first Indian center in Chennai. Through this strategic move, Annalise continues penetrating the world arena with its presence in established and emerging markets like Asia. This translates into a center that specializes in the research and commercialization of new products containing advanced imaging data coupled with computer science that together results in complete AI solutions to support clinical decision-making.

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Report Coverage:

Historical data & forecasts from 2022 to 2029
Growth Opportunities, Challenges, Supply Chain Outlook, Regulatory Framework, Customer Behaviour, and Trend Analysis
Competitive Positioning, Strategies, and Market Share Analysis
Revenue Growth and Forecast Assessment of segments and regions including countries
Company Profiling (Strategies, Products, Financial Information, and Key Developments among others)

Market Segmentation:

The AI In Radiology Market is segmented and analyzed as below:

By Technology

Computer-aided Detection
Auto-segmentation of Organs
Natural Language Processing
Consultation
Quantification and Kinetics
Others

By Application

Mammography
Chest Imaging
Neurology
Cardiovascular
Others

By End-User

Hospitals
Diagnostic Imaging Centers
Others

By Geography

North America
USA
Canada
Mexico
South America
Brazil
Argentina
Others
Europe
Germany
France
United Kingdom
Spain
Others
Middle East and Africa
Saudi Arabia
UAE
Israel
Others
Asia Pacific
China
Japan
India
South Korea
Indonesia
Taiwan
Others


1. INTRODUCTION
1.1. Market Overview
1.2. Market Definition
1.3. Scope of the Study
1.4. Market Segmentation
1.5. Currency
1.6. Assumptions
1.7. Base and Forecast Years Timeline
1.8. Key benefits for the stakeholders
2. RESEARCH METHODOLOGY
2.1. Research Design
2.2. Research Process
3. EXECUTIVE SUMMARY
3.1. Key Findings
4. MARKET DYNAMICS
4.1. Market Drivers
4.2. Market Restraints
4.3. Porter’s Five Forces Analysis
4.3.1. Bargaining Power of Suppliers
4.3.2. Bargaining Power of Buyers
4.3.3. Threat of New Entrants
4.3.4. Threat of Substitutes
4.3.5. Competitive Rivalry in the Industry
4.4. Industry Value Chain Analysis
4.5. Analyst View
5. AI IN RADIOLOGY MARKET BY TECHNOLOGY
5.1. Introduction
5.2. Computer-aided Detection
5.3. Auto-segmentation of Organs
5.4. Natural Language Processing
5.5. Consultation
5.6. Quantification and Kinetics
5.7. Others
6. AI IN RADIOLOGY MARKET BY APPLICATION
6.1. Introduction
6.2. Mammography
6.3. Chest Imaging
6.4. Neurology
6.5. Cardiovascular
6.6. Others
7. AI IN RADIOLOGY MARKET BY END-USER
7.1. Introduction
7.2. Hospitals
7.3. Diagnostic Imaging Centers
7.4. Others
8. AI IN RADIOLOGY MARKET BY GEOGRAPHY
8.1. Introduction
8.2. North America
8.2.1. By Technology
8.2.2. By Application
8.2.3. By End-User
8.2.4. By Country
8.2.4.1. USA
8.2.4.2. Canada
8.2.4.3. Mexico
8.3. South America
8.3.1. By Technology
8.3.2. By Application
8.3.3. By End-User
8.3.4. By Country
8.3.4.1. Brazil
8.3.4.2. Argentina
8.3.4.3. Others
8.4. Europe
8.4.1. By Technology
8.4.2. By Application
8.4.3. By End-User
8.4.4. By Country
8.4.4.1. Germany
8.4.4.2. France
8.4.4.3. United Kingdom
8.4.4.4. Spain
8.4.4.5. Others
8.5. Middle East and Africa
8.5.1. By Technology
8.5.2. By Application
8.5.3. By End-User
8.5.4. By Country
8.5.4.1. Saudi Arabia
8.5.4.2. UAE
8.5.4.3. Israel
8.5.4.4. Others
8.6. Asia Pacific
8.6.1. By Technology
8.6.2. By Application
8.6.3. By End-User
8.6.4. By Country
8.6.4.1. China
8.6.4.2. Japan
8.6.4.3. India
8.6.4.4. South Korea
8.6.4.5. Taiwan
8.6.4.6. Indonesia
8.6.4.7. Others
9. COMPETITIVE ENVIRONMENT AND ANALYSIS
9.1. Major Players and Strategy Analysis
9.2. Market Share Analysis
9.3. Mergers, Acquisitions, Agreements, and Collaborations
9.4. Competitive Dashboard
10. COMPANY PROFILES
10.1. Microsoft Corporation
10.2. Amazon Web Services Inc.
10.3. IBM Corporation
10.4. Rad AI
10.5. Behold.ai
10.6. IMAGEN
10.7. Aidoc
10.8. Koninklijke Philips N.V.
10.9. GE Healthcare
10.10. Siemens Healthcare GmbH

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