DUBLIN--(BUSINESS WIRE)--The "AI In Medical Imaging Market - Global Outlook & Forecast 2022-2027" report has been added to ResearchAndMarkets.com's offering.
The demand for artificial intelligence is constantly increasing in the medical imaging software market. From cardiac events, neurological conditions, fractures, or thoracic complications, artificial intelligence helps physicians to diagnose and provide treatment quickly. Implementing AI in medical imaging has enhanced medical screening, improved precision medicine software, reduced physicians' load, etc.
Technological Advancements Revolutionizing AI in Medical Imaging
- There have been many technological advancements in AI-based medical imaging technologies, which have shown their increasing acceptance in high-income countries. Some of the improvements include the development of integrated rtiI software, which can directly be integrated into imaging equipment (MRI or CT scanner) which facilitates the automation of medical image analysis. Other advances include smartphone technology integration in AI in medical imaging in which front-line health workers could non-invasively screen for various conditions by leveraging a smartphone.
- AI in medical imaging has drawn the attention of several radiologists worldwide. It gives faster and more accurate results and reduces diagnostic errors at reduced costs compared to traditional medical imaging methods. Thus, radiologists believe that AI in medical imaging may bring an enormous opportunity for its increasing implementation in the upcoming years.
SEGMENTATION ANALYSIS
Hospitals are purchasing the artificial intelligence medical software suits as a complete package for the usage or taking up one program at a time which is used the most in the industry. The diagnostic imaging center's significant revenue generation is through imaging procedures, and they are primarily involved in implementing advanced products, which will attract customers.
For instance, AI in medical imaging, along with clinical data, is helping physicians to predict heart attacks in patients accurately.
Neurology has accounted for the dominating share in the industry. The majority of the initial artificial intelligence product development focuses on downstream processing. This Downstream processing majorly includes artificial intelligence for segmentation, detecting anatomical structures, and quantifying a range of pathologies.
Conditions like intracranial hemorrhage, ischemic stroke, primary brain tumors, cerebral metastases, and abnormal white matter signal intensities, which were unmet needs in the industry, has become commercially available solution within the radiology industry.
AI in medical imaging, especially cardiovascular magnetic resonance (CMR), is revolutionized by providing deep learning solutions, especially for image acquisitions, reconstructions, and analysis, which helps in supporting clinical decision-making. CMR is an established tool for routine clinical decision-making, including diagnosis, follow-up, real-time procedures, and pre-procedure planning.
Deep learning methods have enabled more tremendous success in medical image analysis. They have helped high accuracy, efficiency, stability, and scalability. Artificial intelligence tools have become assistive tools in medicine with benefits like error reduction, accuracy, fast computing, and better diagnostics. Natural language processing, Computer Vision, and Context-Aware Computing technologies are also used to create new analysis methods for medical imaging products.
Segmentation by Technology
- Deep Learning
- NIP
- Others
Segmentation by Application
- Neurology
- Respiratory & Pulmonary
- Cardiology
- Breast Screening
- Orthopedic
- Others
Segmentation by Modalities
- CT
- MRI
- X-RAY
- Ultrasound
- Nuclear Imaging
Segmentation by End-User
- Hospitals
- Diagnostic Imaging Centers
- Others
Key Topics Covered:
1 Research Methodology
2 Research Objectives
3 Research Process
4 Scope & Coverage
4.1 Market Definition
4.2 Base Year
4.3 Scope of the Study
5 Report Assumptions & Caveats
6 Market at a Glance
7 Introduction
7.1 Background
7.2 Artificial Intelligence in Healthcare Industry
7.3 Artificial Intelligence in Medical Imaging
8 Market Opportunities & Trends
8.1 Technological Advances in AI-Based Medical Imaging
8.2 Increasing Adoption of AI-Based Medical Imaging in Developed Countries
8.3 Growing Inclination of Radiologists Toward AI-Based Medical Imaging
9 Market Growth Enablers
9.1 Rise in Number of Diagnostic Procedures
9.2 Expanding Investment Opportunities in AI-Based Medical Imaging
9.3 Rise in Approved/Commercial AI-Based Medical Imaging Platforms
9.4 Developments in AI, Cloud-Based, & Hybrid Imaging Solutions
10 Market Restraints
10.1 Lower Adoption of AI-Based Medical Imaging in Lmics
10.2 Lack of Integration and Practical Applications of AI in Medical Imaging
10.3 Concerns Associated With Data Breaches in AI-Based Medical Imaging
11 Market Landscape
11.1 Market Overview
11.2 Market Size & Forecast
11.3 Five Forces Analysis
12 Technology
12.1 Market Snapshot & Growth Engine
12.2 Market Overview
12.3 Deep Learning
12.4 NLP
12.5 Others
13 Application
13.1 Market Snapshot & Growth Engine
13.2 Market Overview
13.3 Neurology
13.4 Respiratory & Pulmonary
13.5 Cardiology
13.6 Breast Cancer
13.7 Orthopedic
13.8 Others
14 Modality
14.1 Market Snapshot & Growth Engine
14.2 Market Overview
14.3 CT
14.4 MRI
14.5 X-Ray
14.6 Ultrasound
14.7 Nuclear Imaging
15 End-user
15.1 Market Snapshot & Growth Engine
15.2 Market Overview
15.3 Hospitals
15.4 Diagnostic Imaging Centers
15.5 Others
16 Geography
16.1 Market Snapshot & Growth Engine
16.2 Geographic Overview
17 North America
18 Europe
19 APAC
20. Latin America
21. Middle East & Africa
22. Competitive Landscape
22.1 Competition Overview
22.2 Market Share Analysis
23. Key Company Profiles
23.1 General Electric
23.2 Siemens Healthineers
23.3 Koninklijke Philips
23.4 IBM Watson Health
24. Other Prominent Vendors
25. Report Summary
25.1 Key Takeaways
25.2 Strategic Recommendations
26. Quantitative Summary
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