Fuelling Digital Pathology Advances Using Artificial Intelligence, 2020 Research Report - ResearchAndMarkets.com

DUBLIN--(BUSINESS WIRE)--The "Fuelling Digital Pathology Advances Using Artificial Intelligence" report has been added to ResearchAndMarkets.com's offering.

The astonishing progress of combinatorial chemistry, the development of high throughput screening techniques, parallel computing, and quantum mechanics modeling approaches, among many other cutting-edge innovations, has derived in a Big Data explosion.

In parallel, the advent of structural biology, the omics revolution, and the extraordinary progress of computational science and communication technologies, have enables to face the most concerning challenges associated with the Big Data revolution: complexity, high scaling, speedy growth, source diversity, structure level, and uncertainty.

Artificial intelligence (AI)-driven platforms hold the promise to smartly embody and reshape the most remarkable advances simultaneously reaching their apogee in the present era to energize the medical diagnostics industry with novel, smart approaches to digital pathology.

The real potential of artificial intelligence (AI) to revolutionize digital pathology is just beginning to be noticed. AI-driven technology capabilities range from the acceleration of the computational analysis of atomic and molecular properties in the tissue, the bases of de novo diagnostics strategies, and drug response comparison, to the optimization of companion diagnostics methodologies and post-treatment surveillance policies.

Key Topics Covered

1. Executive Summary

1.1 Research Focus

1.2 Research Scope

1.3 Analysis Framework

1.4 Research Methodology

2. Technology Landscape and Trends

2.1 Role of Artificial Intelligence in Digital Pathology

2.2 AI-based Tools Driving Medical Diagnostics

2.3 Key Aspects to Consider for Further Evolution

2.4 Key Definitions and Landscape Notes

2.5 Current Workflow and Future Possibilities

2.6 Breakdown of the Main Technologies in Digital Pathology

3. Technology Status Review and Assessment

3.1 Major Concepts in AI-driven Digital Pathology

3.2 Principal Aspects of AI-based Analysis Algorithms

3.3 Efficient WSI Integration for Improved Workflow

3.4 Strategic Technology Integration

3.5 Phased Workflow Implementation

3.6 AI-driven Digital Pathology Main Stakeholders

4. Business Landscape and Intellectual Property

4.1 IP Global Analysis by Assignee

4.2 IP Analysis by Academic Institution and Region

4.3 Year-wise Global IP Analysis

4.4 Repercussions in Businesses and Processes

4.5 Strategic Imperatives for Future Work

4.6 AI-driven Digital Pathology Cluster of Services

4.7 AI-driven Digital Pathology Vendor Ecosystem

4.8 Business Model Stratification and Assessment

4.9 Big Data as Enabler of AI-driven Digital Pathology

4.10 Datasets Generators for Regulatory Validation

4.11 Mainstream Clinical Applications in Focus

5. Technology Radar and Intelligent Solutions

5.1 Healthcare Ecosystem in 2025

5.2 AI-driven Digital Pathology Solutions Mapping

5.3 Technologies Coming in the Next Five Years

5.4 Roadmap for AI-driven Digital Pathology

6. Market Potential and Industry Evolution

6.1 Global Digital Pathology Market Forecast

6.2 Global Digital Pathology Market Forecast by Product

6.3 Global Digital Pathology Market Forecast by Application

6.4 Global Digital Pathology Market Forecast by End-user

6.5 Global Digital Pathology Market Forecast by Region

7. Funding and Investment Landscape

7.1 Private Equity Opportunities in Digital Pathology

7.2 Sustained Commitment to AI/DP Supporting M&As

7.3 Aerospace & Defense Firms Investing in AI/DP

7.4 Aerospace & Defense Principal M&A Transactions

7.5 Government IT Systems Small Business Innovation

8. AI-based Companies to Watch

8.1 Aiforia

8.2 ContextVision

8.3 Deciphex

8.4 IndicaLabs

8.5 Barco

8.6 Inspirata

8.7 Leica Biosystems

8.8 Flagship Biosciences

8.9 Regional Focus - Latin America

9. Transformational Transactions During 2013-2019

9.1 Industry Convergence in Healthcare Transformation

9.2 Comparison of Major Deals - Up to $34 Billion

9.3 Comparison of Major Deals - Up to $2 Billion

9.4 Comparison of Major Deals - Up to $750 Million

9.5 Comparison of Major Deals - Up to $290 Million

9.6 Comparison of Major Deals - Up to $195 Million

9.7 Comparison of Major Deals - Up to $156 Million

9.8 Comparison of Major Deals - Up to $105 Million

9.9 Comparison of Major Deals - Up to $75 Million

9.10 Comparison of Major Deals - Up to $60 Million

9.11 Comparison of Major Deals - Up to $50 Million

9.12 Comparison of Major Deals - Up to $38 Million

9.13 Comparison of Major Deals - Up to $30 Million

9.14 Comparison of Major Deals - Up to $22 Million

9.15 Comparison of Major Deals - Up to $12 Million

9.16 Comparison of Major Deals - Up to $10 Million

9.17 Comparison of Major Deals - Up to $5 Million

9.18 Comparison of Major Deals - Up to $3 Million

9.19 Comparison of Major Deals - Up to $1 Million

9.20 Comparison of Major Deals - Other Agreements

9.21 US Vendors in AI-drive Healthcare Systems

10. Key Industry Influencers

10.1 Key Industry Participants

10.2 Key Industry Integrators

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