Artificial Intelligence (AI) In Radiology Workflow Optimization Market Size, Share, Opportunities, And Trends By Technology (Machine Learning, Deep Learning, Natural Language Processing (Nlp), Computer Vision, Others), By Application (Image Acquisition And Preprocessing, Image Analysis And Interpretation, Reporting And Documentation, Quality Control And Assurance, Others), By End-User (Hospitals And Clinics, Diagnostic Imaging Centers, Research Institutes And Academic Centers, Others), And By Geography - Forecasts From 2023 To 2028

  • Published : Oct 2023
  • Report Code : KSI061615947
  • Pages : 131

The AI in radiology workflow optimization market is estimated to grow at a CAGR of 32.12% during the forecast period.

The discipline of radiology workflow optimisation has been transformed by AI, ushering in a new era of precision and efficiency. AI-powered solutions have emerged as game changers as the demand for faster and more accurate diagnostics grows. AI supports radiologists in analysing medical pictures by leveraging powerful algorithms and machine learning, assisting in early identification, and lowering diagnostic mistakes. AI optimises productivity by automating time-consuming processes like image segmentation and anomaly detection, allowing radiologists to focus on complicated situations. The AI in radiology workflow optimisation market is expanding rapidly, with major healthcare providers and imaging centres using these technologies. AI's incorporation into radiology operations promises to alter healthcare delivery by improving patient outcomes, lowering costs, and streamlining processes.

Automation of Repetitive Tasks Enhances the AI in Radiology Workflow Optimization Market Growth.

The automation of repetitive processes is critical in altering the efficiency of radiology practises in the AI in radiology workflow optimisation market. AI-powered algorithms can evaluate massive volumes of medical imaging data, such as X-rays and MRI scans, in record time to discover common patterns and abnormalities. Radiologists can save time and focus on more complicated and crucial cases by automating processes such as picture segmentation, feature extraction, and comparison with past instances. This process simplification not only increases radiology productivity, but it also leads to faster diagnosis and better patient care. Automation improves accuracy and consistency, which benefits both healthcare practitioners and patients.

Reduction in Radiologist Workload in AI in Radiology Workflow Optimization Market.

The use of AI in the radiology workflow optimisation market has resulted in a significant decrease in radiologist burden. AI algorithms for triaging chest X-rays lowered the radiologist's labour by up to 80%, according to research published in the Journal of the American College of Radiology. Another study published in Nature discovered that AI-based solutions increased radiologists' productivity in identifying breast cancer by 21%. the use of artificial intelligence to automate common operations such as image processing and report production allows radiologists to focus on more complicated and essential cases, resulting in shorter turnaround times and better patient care. Reduced workload paired with improved accuracy results in a more efficient and successful radiology process.

Integration of AI with PACS and RIS Systems Boosts the AI in Radiology Workflow Optimization Market Size.

The integration of AI with Picture Archiving and Communication Systems (PACS) and Radiology Information Systems (RIS) has transformed the AI market for radiology workflow optimisation. According to research published in the Journal of Digital Imaging, AI algorithms combined with PACS greatly enhanced lung nodule identification, with a sensitivity of 90% and a specificity of 89%. Another study by the European Society of Radiology found that integrating AI with RIS reduced report turnaround time by 30%. The seamless collaboration of AI with these technologies allows for faster data processing, automated report production, and simplified processes, which improves radiologists' productivity and patient care.

North America is the Market Leader in the AI in Radiology Workflow Optimization Market.

North America has emerged as the market leader in AI in radiology workflow optimisation market. The region's supremacy may be linked to its strong healthcare infrastructure, early adoption of AI technology, and extensive R&D efforts. Furthermore, North America is home to several renowned AI and healthcare technology businesses that are driving industry innovation. The region's emphasis on precision medicine and patient-centred care has resulted in considerable investments in AI-driven radiology technologies, which have piqued the interest of healthcare providers and institutions. North America is projected to maintain its dominant position in this fast-changing industry as AI continues to grow and gain acceptance.

Faster Turnaround Time for Reports in AI in Radiology Workflow Optimization Market.

The use of AI in the radiology workflow optimisation market has resulted in considerably shorter report turnaround times. According to research published in the American Journal of Roentgenology, AI-powered solutions lowered report turnaround times for radiological tests by up to 50%. Furthermore, according to a study published in the Journal of Digital Imaging, AI algorithms combined with Picture Archiving and Communication Systems (PACS) increased the speed of recognising key results by 30%. The automation of image analysis and report preparation enables radiologists to provide rapid and reliable findings, resulting in faster diagnoses and better patient care. The use of AI technology has aided in the speeding of radiology reporting procedures, which benefits both healthcare practitioners and patients.

Key Developments:

  • In June 2023, Nuance® Communications, Inc., a Microsoft Company, and Epic announced that the Nuance® Dragon® Ambient eXperience Express solution will be made accessible to the Epic community. Building on Microsoft, Nuance, and Epic's overall strategic collaboration, with clinical documentation that writes itself, the integration of DAX Express into Epic processes will function as a copilot for Dragon Medical customers to further control administrative tasks that contribute to burnout, extend patient access to care, and improve healthcare outcomes.
  • In November 2021, NANO-X IMAGING LTD, an innovative medical imaging technology company, has completed its previously announced merger with Zebra Medical Vision, Ltd., a deep-learning medical imaging analytics company, in an all-stock transaction valued at approximately $110 million at closing, with up to $84 million more in additional stock for the completion of various performance milestones. Zebra is currently known as Nanox.AI.

Company Products:

  • SmartCurve™ Breast Stabilization System: This artificial intelligence-enhanced technology is intended to increase patient comfort during mammography. The technology employs artificial intelligence (AI) algorithms to analyse breast structure and apply customised compression, resulting in a more pleasant experience for patients and improved picture quality for radiologists.
  • Agfa HealthCare Enterprise Imaging: AI-powered apps automate picture processing, segmentation, and quantification on this integrated imaging platform. AI systems help radiologists analyse and interpret medical pictures more effectively, resulting in faster diagnosis and more productivity.
  • AI-Rad Companion: The AI-Rad Companion package from Siemens Healthineers offers a number of AI-powered tools meant to help radiologists in their daily practice. These programmes utilise artificial intelligence algorithms to automate image processing, detect anomalies, and offer quantitative data, therefore speeding the radiology workflow and improving diagnostic accuracy.
  • Nuance PowerScribe One: This AI-powered radiology reporting platform automates the preparation of radiology reports with powerful natural language processing (NLP) and voice recognition technologies. It supports radiologists in effectively producing complete and accurate reports, lowering turnaround time and increasing productivity.


  • By Technology
    • Machine Learning
    • Deep Learning
    • Natural Language Processing (Nlp)
    • Computer Vision
    • Others          
  • By Application
    • Image Acquisition And Preprocessing
    • Image Analysis And Interpretation
    • Reporting And Documentation
    • Quality Control And Assurance
    • Others       
  • By End-User
    • Hospitals And Clinics
    • Diagnostic Imaging Centers
    • Research Institutes And Academic Centers
    • Others                   
  • By Geography
    • North America
      • United States
      • Canada
      • Mexico
    • South America
      • Brazil
      • Argentina
      • Others
    • Europe
      • United Kingdom
      • Germany
      • France
      • Italy
      • Spain    
      • Others
    • Middle East and Africa
      • Saudi Arabia
      • UAE
      • Others
    • Asia Pacific
      • Japan
      • China
      • 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. Sources

2.3. Research Design


3.1. Research Highlights


4.1. Market Drivers

4.2. Market Restraints

4.3. Porters 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. Machine Learning

5.3. Deep Learning

5.4. Natural Language Processing (NLP)

5.5. Computer Vision

5.6. Others          


6.1. Introduction

6.2. Image Acquisition and Preprocessing

6.3. Image Analysis and Interpretation

6.4. Reporting and Documentation

6.5. Quality Control and Assurance

6.6. Others       


7.1. Introduction

7.2. Hospitals and Clinics

7.3. Diagnostic Imaging Centers

7.4. Research Institutes and Academic Centers

7.5. Others                   


8.1. Introduction

8.2. North America

8.2.1. United States

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. United Kingdom

8.4.2. Germany

8.4.3. France

8.4.4. Italy

8.4.5. Spain

8.4.6. Others

8.5. Middle East and Africa

8.5.1. Saudi Arabia

8.5.2. UAE

8.5.3. Others

8.6. Asia Pacific

8.6.1. Japan

8.6.2. China

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, Acquisitions, Agreements, and Collaborations

9.4. Vendor Competitiveness Matrix


10.1. Aidoc Medical Ltd.

10.2. Zebra Medical Vision Ltd.

10.3. Enlitic, Inc.

10.4. Butterfly Network, Inc.

10.5. IBM Watson Health (a division of IBM Corporation)

10.6. Siemens Healthineers AG

10.7. GE Healthcare (a division of General Electric Company)

10.8. NVIDIA Corporation

10.9. Imagen Technologies, Inc.

10.10. Koninklijke Philips N.V.      

Aidoc Medical Ltd.

Zebra Medical Vision Ltd.

Enlitic, Inc.

Butterfly Network, Inc.

Ibm Watson Health (A Division Of Ibm Corporation)

Siemens Healthineers Ag

Ge Healthcare (A Division Of General Electric Company)

Nvidia Corporation

Imagen Technologies, Inc.

Koninklijke Philips N.V.  

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