Artificial Intelligence (AI) In Radiology Market Size, Share, Opportunities, COVID-19 Impact, And Trends 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) And By Geography - Forecasts From 2023 To 2028

  • Published : May 2023
  • Report Code : KSI061614385
  • Pages : 145

AI in radiology market is expected to grow at a CAGR of 29.4% from an estimated market size of US$1,058.824 million in 2021 to reach US$6,433.214 million in 2028.

Deep learning algorithms used in artificial intelligence (AI) have made significant advancements in visual identification applications. The domain of medical image analysis is developing quickly due to several implementations of techniques like variational autoencoders and convolutional neural networks. In contrast to traditional qualitative evaluations of radiographic qualities, AI techniques excel at automatically spotting intricate patterns in imaging data. In radiology, artificial intelligence (AI) algorithms are created to quantify particular radiographic properties, such as the 3D geometry of a tumor or the intratumoral texture and distribution of pixel intensities.

In radiography, qualified medical professionals visually evaluate medical pictures and record conclusions to locate, describe, and track diseases. Such evaluation is frequently dependent on knowledge and experience and is occasionally susceptible to opinion. In comparison to such subjective analysis, AI is excellent at seeing intricate patterns in imaging data and can automatically deliver a quantitative assessment. When AI is incorporated into the medical system as a tool to aid doctors, radiological assessments can then be conducted with greater accuracy and reproducibility.

Growing applications and ventures by major medical firms to incorporate AI in healthcare have been major drivers of the AI in radiology market.

The use of artificial intelligence (AI) in medical imaging, including image processing, radiography, and interpretation, is one of the most prospective sectors of health innovation. As technology progresses in many areas of healthcare, software integrating artificial intelligence (AI), such as machine learning (ML) technology and systems, has become an increasingly important part of several medical equipment. The capacity of machine learning to derive useful and essential insights from the enormous amounts of data acquired daily in the healthcare industry is one of its most important advantages. When applied to radiology data such as traditional radiography, CT, MRI, and PET scans as well as radiology reports, machine learning systems, and the software automatically recognize complicated patterns and assist doctors in making informed decisions.

Furthermore, there are several start-ups that have received support from the government to promote AI for radiology purposes. For instance,, an Indian start-up started in 2016 and supported by the Indian government, employs deep learning algorithms to analyze CT, X-ray, and MRI images to detect disease and generate automated diagnostic reports. The company has received support from the government through the NITI Aayog, a government resource center, and its radiology solutions have been implemented in several states of India.

Recent Developments:

  • In February 2023, photographic film supplier Fujifilm Australia announced the expansion of its line of artificial intelligence products into the healthcare business by collaborating with Australian imaging experts and their CXR Edge solution, a chest X-ray AI decision-support solution for mobile and stationary X-ray equipment. As its Medical Systems division has grown in recent years, Fujifilm has achieved some notable landmarks, including the creation of Fuji Computed Radiography, which employs the first digital method for digitizing X-ray pictures ever devised. The businesses worked together to create Annalise CXR Edge, a software medical gadget for mobile and fixed X-ray machines that would aid point-of-care doctors and radiologists in the interpretation of chest X-rays. The device's AI system detects the presence of radiological discoveries and notifies the user of potential findings in less than 10 seconds.
  • In February 2023,, an AI platform for radiology screening that improves the identification of common illnesses, announced a €7 million series A investment, increasing its capital base to €10 million. The solution offered by Avicenna.AI employs image-trained deep learning to recognize, identify, and quantify life-threatening illnesses in radiology laboratories. The Avicenna.AI platform is instructed using CT scan imaging that radiologists use to prioritize patients who are exhibiting symptoms before a diagnosis has been established. The AI software offers two variants, one to analyze symptoms and the danger of a heart attack and the other to detect brain injury and stroke risk. The platform from Avicenna.AI is used to categorize the severity of patient conditions, assisting radiologists in deciding whether the patient's life is in danger.

Based on application, the AI in radiology market is expected to witness strong growth in the neurological treatment sector.

Using volumetric tumor segmentation as the basis for its work, artificial intelligence (AI) can help enhance the identification and detection of brain tumors and other neurological cancers with high accuracy and consistency. The system can also automatically locate brain tumors on MRI scans. These methods can be very helpful in making accurate diagnoses as well as helping to track the effectiveness of tumor therapy in a repeatable and unbiased manner. Outcome prediction is another way AI is used in neuro-oncology. Machine learning techniques have been developed to forecast preoperative glioma survival using MRI-based blood volume distribution data.

Asia Pacific is anticipated to hold a sizable portion of the AI in radiology market.

Based on geography, the AI in radiology market is segmented into North America, South America, Europe, the Middle East and Africa, and Asia Pacific. Due to increased research spending and advancements in the medical and biotech industries in the area, the Asia Pacific is anticipated to hold significant shares of the AI in radiology market. Additionally, the presence of a sizable patient base is anticipated to boost the requirement for enhanced treatment facilities and stimulate the growth of the healthcare sector, which will assist the expansion of AI in radiology in the area. The market in Asia Pacific has also benefited from expenditure in the healthcare sector, particularly to develop and incorporate new technological improvements. The region's economies are putting more of an emphasis on building a strong healthcare system for patient diagnosis and treatment.

AI in Radiology Market Scope:


Report Metric Details
Market Size Value in 2021
US$1,058.824 million
Market Size Value in 2028
US$6,433.214 million
Growth Rate CAGR of 29.4% from 2021 to 2028
Base Year 2021
Forecast Period 2023–2028
Forecast Unit (Value) USD Million
Segments Covered Technology, Application, End-User, And Geography
Regions Covered North America, South America, Europe, Middle East and Africa, Asia Pacific
Companies Covered Microsoft Corporation, Amazon Web Services Inc., IBM Corporation, Rad AI,, IMAGEN, Aidoc, Koninklijke Philips N.V, GE Healthcare, Siemens Healthcare GmbH
Customization Scope Free report customization with purchase


Market Segmentation:

  • 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


1.1. 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



2.1. Research Data

2.2. Research Design



3.1. Research Highlights



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



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



6.1. Introduction

6.2. Mammography

6.3. Chest Imaging

6.4. Neurology

6.5. Cardiovascular

6.6. Others



7.1. Introduction

7.2. Hospitals

7.3. Diagnostic Imaging Centers

7.4. Others



8.1. Introduction

8.2. North America

8.2.1. USA

8.2.2. Canada

8.2.3. Mexico

8.3. South America

8.3.1. Brazil

8.3.2. Argentina

8.3.3. Others

8.4. Europe

8.4.1. Germany

8.4.2. France

8.4.3. United Kingdom

8.4.4. Spain

8.4.5. Others

8.5. Middle East and Africa

8.5.1. Saudi Arabia

8.5.2. UAE

8.5.3. Israel

8.5.4. Others

8.6. Asia Pacific

8.6.1. China

8.6.2. Japan

8.6.3. India

8.6.4. South Korea

8.6.5. Indonesia

8.6.6. Taiwan

8.6.7. Others



9.1. Major Players and Strategy Analysis

9.2. Emerging Players and Market Lucrativeness

9.3. Mergers, Acquisition, Agreements, and Collaborations

9.4. Vendor Competitiveness Matrix



10.1. Microsoft Corporation

10.2. Amazon Web Services Inc. 

10.3. IBM Corporation 

10.4. Rad AI


10.6. IMAGEN

10.7. Aidoc

10.8. Koninklijke Philips N.V.

10.9. GE Healthcare

10.10. Siemens Healthcare GmbH

Microsoft Corporation

Amazon Web Services Inc.

IBM Corporation

Rad AI



Koninklijke Philips N.V.

GE Healthcare

Siemens Healthcare GmbH