The 'Deep Learning Market: Focus on Medical Image Processing, 2020-2030' report features an extensive study on the current market landscape offering an informed opinion on the likely adoption of such solutions over the next decade. The study presents an in-depth analysis, highlighting the capabilities of various stakeholders engaged in this domain.
One of the key objectives of this report was to estimate the existing market size and the future growth potential within the deep learning market (medical image processing segment), such as global radiology spending across countries, number of radiologists employed across different regions of globe, annual salary of radiologists, rate of adoption of deep learning-based solutions, we have developed informed estimates on the financial evolution of the market, over the period 2020-2030.
Moreover, several technology-focused innovators, such as (in alphabetical order) IBM, GE Healthcare and Google, have entered into strategic alliances with big pharma players, in order to bring proprietary deep learning-based medical solutions to the market. This upcoming segment of the pharmaceutical industry that exists at the interface between medicine and information technology, has garnered the attention of prominent venture capital firms and strategic investors. In the long term, the market is anticipated witness significant growth as more machine learning based solutions are approved for use.
Scope of the Report
A detailed review of the current market landscape of deep learning solutions for medical image processing, along with information on their status of development (launched/under development), regulatory approvals (FDA, CE mark, others), type of offering (diagnostic software/tool, diagnostic software/tool + device), type of image processed (X-ray, MRI, CT, ultrasound), application area (lung infections/respiratory disorders, brain injuries/disorders, lung cancer, cardiac conditions/cardiovascular disorders, bone deformities/orthopedic disorders, breast cancer and others).
In addition, it presents details of companies developing such solutions, such as their year of establishment, company size, location of headquarters and focus area (in terms of type of deployment model). Further, it highlights key features of each solution and affiliated technologies.
An in-depth analysis of the contemporary market trends, presented using three schematic representations, including [A] a grid representation illustrating the distribution of solutions based on application area, type of image processed and type of offering and [B] an insightful map representation highlighting the geographical activity of the players. Elaborate profiles of key players that are engaged in the development of deep learning-based solutions intended for processing of medical images. Each company profile features a brief overview of the company (including information on year of establishment, number of employees, location of headquarters and key members of the executive team), details of their respective portfolio of solutions, recent developments and an informed future outlook.
Key Questions Answered
Who are the leading developers of deep learning-based solutions for medical image processing?
What are the key application areas for deep learning solutions designed for processing of medical images, such as X-Ray, ultrasound, CT, MRI and others?
How many solutions based on deep learning technology for processing of medical images have been cleared by FDA or have received CE marking?
What is the impact of COVID-19 on the demand for deep learning solutions designed for processing of medical images?
What is the likely valuation/net worth of companies involved in this segment?
What is the likely cost saving potential associated with the use of deep learning-based solutions for processing of medical images?
How is the current and future opportunity likely to be distributed across key market segments?
What is the potential usability of deep learning-based medical image processing solutions for lung scanning in COVID-19 patients?
Which partnership models are commonly adopted by stakeholders in this industry?
What is the overall trend of funding and investments in this domain?
What are the opinions of key opinion leaders involved in the deep learning space?
Key Topics Covered:
1. PREFACE 1.1. Scope of the Report 1.2. Research Methodology 1.3. Chapter Outlines
2. EXECUTIVE SUMMARY
3. INTRODUCTION 3.1. Humans, Machines and Intelligence 3.2. The Science of Learning 3.3. Artificial Intelligence 3.4. The Big Data Revolution 3.5. Applications of Deep Learning in Healthcare
4. CASE STUDY: IBM WATSON VERSUS GOOGLE DEEPMIND 4.1. Chapter Overview 4.2. International Business Machines (IBM) 4.3. Google 4.4. IBM versus Google: Artificial Intelligence-related Acquisitions 4.5. IBM versus Google: Healthcare Focused Partnerships and Collaborations 4.6. IBM versus Google: Primary Concerns and Future Outlook
5. MARKET OVERVIEW 5.1. Chapter Overview 5.2. Deep Learning in Medical Image Processing: Overall Market Landscape 5.3. Deep Learning in Medical Image Processing: Information on Key Characteristics 5.4. Deep Learning in Medical Image Processing: List of Companies
7. PARTNERSHIPS AND COLLABORATIONS 7.1. Chapter Overview 7.2. Partnership Models 7.3. Deep Learning in Medical Image Processing: List of Partnerships and Collaborations 7.4. Concluding Remarks
8. FUNDING AND INVESTMENT ANALYSIS 8.1. Chapter Overview 8.2. Types of Funding 8.3. Deep Learning in Medical Image Processing: Recent Funding Instances
9. COMPANY VALUATION ANALYSIS 9.1. Chapter Overview 9.2. Methodology 9.3. Categorization by Parameters
10. CASE STUDY: ANALYSIS OF DEEP LEARNING-BASED CLINICAL TRIALS REGISTERED IN THE US 10.1. Chapter Overview 10.2. Scope and Methodology 10.3 Clinical Trial Analysis
11. PATENT ANALYSIS 11.1. Chapter Overview 11.2. Scope and Methodology 11.3. Deep Learning and Medical Image Processing: Patent Analysis 11.4. Patent Valuation Analysis
12. COST SAVING ANALYSIS 12.1. Chapter Overview 12.2. Key Assumptions and Methodology 12.3. Overall Cost Saving Potential of Deep Learning in Medical Image Processing Solutions, 2020-2030 12.4. X-Ray Images 12.5. MRI Images 12.6. CT Images 12.7. Ultrasound Images 12.8. Concluding Remarks: Cost Saving Scenarios
13. MARKET FORECAST 13.1. Chapter Overview 13.2 Forecast Methodology and Key Assumptions 13.3 Overall Deep Learning in Medical Image Processing Market 13.3 Deep Learning in Medical Image Processing Market: Distribution by Application Area 13.4 Deep Learning in Medical Image Processing Market: Distribution by Type of Image Processed 13.5 Deep Learning in Medical Image Processing Market: Distribution by Key Geographical Regions 13.6 Concluding Remarks
14. DEEP LEARNING IN HEALTHCARE: EXPERT INSIGHTS 14.1. Chapter Overview 14.2. Industry Experts 14.3. University and Hospital Experts 14.4. Other Expert Opinions
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