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Global Artificial Intelligence (AI) in Diagnostics Market Analysis
- Published Date: December, 2022 | Report ID: CLS-1836 | No of pages: 225 | Format:
Global Artificial Intelligence (AI) in Diagnostics Market Executive Summary
The healthcare sector is turning its attention to a variety of AI-based technologies in order to effectively handle the constantly growing number of massive and complicated medical diagnostic data. Healthcare professionals may now help patients through cutting-edge treatment modalities thanks to the extensive rise of digital health. With the use of AI technology, doctors can better diagnose and treat patients. Also, during the pandemic, AI-powered diagnostics were crucial in identifying and diagnosing coronavirus strains in patients.
Market Size and Key Findings
The Global Artificial Intelligence (AI) in Diagnostics Market size is at around US $ xx Bn in 2021 and is projected to reach US $ xx Bn in 2030, exhibiting a CAGR of xx% during the forecast period.
Global Artificial Intelligence (AI) in Diagnostics Market Size (In USD Bn)
(2021-2030F)
Market Dynamics
Market Growth Drivers Analysis
Large and complicated data are produced at various stages of the care delivery process as a result of the growing digitalization and use of information technologies in the healthcare sector. In the field of medical diagnostics, big data includes, among other things, the information generated by clickstream and online & social media interactions, as well as readings from medical equipment including sensors, ECGs, X-rays, and other imaging devices, as well as medical claims and other billing records. It also includes biometric data. With the increasing acceptance of EHRs, digital laboratory slides, and high-resolution radiological images among healthcare practitioners over the past ten years, big data and analytical solutions have become exponentially more sophisticated and widely used. Particularly in the US, one of the top five big data businesses in healthcare. The usage of bidirectional patient portals, which enable patients to upload data and photos to their EMRs, is anticipated to result in a rise in the volume of big data in medical diagnostics in the upcoming years
Market Restraints
With the use of AI technology, doctors can better diagnose and treat patients. However, it has been noted that doctors are reluctant to adopt new technology. For instance, doctors mistakenly believe that AI would eventually supplant them in the medical field. Because empathy and persuasion are considered to be human abilities, doctors and radiologists think that the use of technology cannot entirely replace the need for a physician. It's also feared that patients may have an unhealthy preference for these technologies and avoid critical in-person treatments, which might put long-term doctor-patient relationships at risk. Many medical practitioners are skeptical of AI's ability to correctly diagnose patient problems.
As a result, it is difficult to persuade providers that AI-based solutions are economical, effective, and secure options that provide doctors with convenience and improved patient care. Healthcare organizations are, however, more open to the potential advantages of AI-based solutions and the variety of fields they may be used in. As a result, it's possible that in the years to come, physicians and radiologists may be more receptive to AI-based healthcare technology.
Competitive Landscape
Key Players
The prominent players operating in this market include Aidoc; AliveCor, Inc.; Vuni, Inc.; Digital Diagnostics, Inc.; Siemens Healthineers; Neural Analytics; Riverain Technologies; Zebra Medical Vision, Inc; GE Healthcare; Imagen Technologies
TABLE OF CONTENT
1. Report Description of the Global Artificial Intelligence (AI) in Diagnostics Market Segmentation
1.1 Research Scope and Assumption
1.2 Objective of the study
1.3 Research Methodology
1.4 Reason to buy the report
2. Global Artificial Intelligence (AI) in Diagnostics Market Segmentation Executive Summary
2.1 Global Artificial Intelligence (AI) in Diagnostics Market Segmentation –Industry Snapshot & key buying criteria, 2021-2030
2.2 Market Size, Growth Prospects, and Key findings
3. Market Dynamics of Global Artificial Intelligence (AI) in Diagnostics Market Segmentation
3.1 Market Growth Drivers Analysis
3.2 Market Restrains Analysis
4. Global Artificial Intelligence (AI) in Diagnostics Market Segmentation Segmentation
4.1 By Component Outlook
4.1.1 Software
4.1.2 Hardware
4.1.3 Services
4.2 By Diagnosis Outlook
4.2.1 Cardiology
4.2.2 Oncology
4.2.3 Pathology
4.2.4 Radiology
4.2.5 Chest and Lung
4.2.6 Neurology
4.2.7 Others
5. Global Artificial Intelligence (AI) in Diagnostics Market Segmentation Share
5.1 Market Analysis, Insights, and Forecast – By Revenue
6. Competitive Landscape
6.1 Major Top Market Players
7. Key Company Profiles
7.1 Company 1
7.1.1 Overview
7.1.2 Financial Performance
7.1.3 Product & Services
7.1.4 Strategic initiatives
7.2 Company 2
7.2.1 Overview
7.2.2 Financial Performance
7.2.3 Product & Services
7.2.4 Strategic initiatives
7.3 Company 3
7.3.1 Overview
7.3.2 Financial Performance
7.3.3 Product & Services
7.3.4 Strategic initiatives
7.4 Company 4
7.4.1 Overview
7.4.2 Financial Performance
7.4.3 Product & Services
7.4.4 Strategic initiatives
7.5 Company 5
7.5.1 Overview
7.5.2 Financial Performance
7.5.3 Product & Services
7.5.4 Strategic initiatives
8. Healthcare Policies and Regulatory Landscape
8.1 Healthcare Policies in Global
8.2 Regulatory Framework in Global
8.3 Pricing & Reimbursement Scenario in Global Artificial Intelligence (AI) in Diagnostics Market Segmentation
9. Factors Driving Future Growth
9.1 New Trends and Development Global Artificial Intelligence (AI) in Diagnostics Market Segmentation
9.2 Future Opportunities
10. Strategic Recommendations
Segmentation
Artificial Intelligence (AI) in Diagnostics Market Segmentation
- By Component Outlook Type (Revenue, USD Billion):
- Software
- Hardware
- Services
- By Diagnosis Outlook Type (Revenue, USD Billion):
- Cardiology
- Oncology
- Pathology
- Radiology
- Chest and Lung
- Neurology
- Others
Methodology
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