Deep Learning in Drug Discovery and Diagnostics, 2035: Google, Amazon, Facebook, LinkedIn, IBM and Netflix, are Already Using Deep Learning Algorithms to Analyze Users' Activities

Dublin, March 28, 2017 -- Research and Markets has announced the addition of the "Deep Learning in Drug Discovery and Diagnostics, 2017 - 2035" report to their offering.

The Deep Learning: Drug Discovery and Diagnostics Market, 2017-2035 report examines the current landscape and future outlook of the growing market of deep learning solutions within the healthcare domain. Primarily driven by the big data revolution, deep learning algorithms have emerged as a novel solution to generate relevant insights from medical data.

This continuing shift towards digitalization of healthcare system has been backed by a number of initiatives taken by the government, and has also sparked the interest of several industry / non-industry players. The involvement of global technology companies and their increasing collaborations with research institutes and hospitals are indicative of the research intensity in this field. At the same time, the pharma giants have been highly active in adopting the digital models. Companies such as AstraZeneca, Pfizer and Novartis continue to evaluate the digital health initiatives across drug discovery, clinical trial management and medical diagnosis.

Some notable examples of such digital health initiatives include GSK and Pfizer's collaboration with Apple for the use of the latter's research kit in clinical trials, Biogen's partnership with Fitbit for using smart wearables in clinical trial management, and Teva Pharmaceuticals' partnership with American Well to use Smart Inhalers for patients with asthma and COPD.

Backed by funding from several Venture Capital firms and strategic investors, deep learning has emerged as one of the most widely explored initiatives within digital healthcare. The current generation of deep learning models are flexible and have the ability to evolve and become more efficient over time. Despite being a relatively novel field of research, these models have already demonstrated significant potential in the healthcare industry.

One of the key objectives of this study was to identify the various deep learning solutions that are currently available / being developed to cater to unmet medical needs, and also evaluate the future prospects of deep learning within the healthcare industry. These solutions are anticipated to open up significant opportunities in the field of drug discovery and diagnostics as the healthcare industry gradually shifts towards digital solutions.

Companies, such as Google, Amazon, Facebook, LinkedIn, IBM and Netflix, are already using deep learning algorithms to analyze users' activities and make customized suggestions and recommendations based on individual preferences. Today, in many ways, deep learning algorithms have enabled computers to see, read and write. In light of recent advances, the error rate associated with machines being able to analyze and interpret medical images has come down to 6%, which, some research groups claim, is even better than humans.

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. Artificial Intelligence
3.3. The Science of Learning
3.3.1. Teaching Machines
3.3.1.1. Machines for Computing
3.3.1.2. Understanding Human Brain: Way to Artificial Intelligence
3.4. The Big Data Revolution
3.4.1. Big Data: An Introduction
3.4.2. Big Data: Internet of Things (IoT)
3.4.3. Big Data: A Growing Trend
3.4.4. Big Data: Application Areas
3.4.4.1. Big Data Analytics in Healthcare: Collaborating For Value
3.4.4.2. Machine Learning
3.4.4.3. Deep Learning: The Amalgamation of Machine Learning and Big Data
3.5. Deep Learning in Healthcare
3.5.1. Personalized Medicine
3.5.2. Lifestyle Management
3.5.3. Wearable Devices
3.5.4. Drug Discovery
3.5.5. Clinical Trial Management
3.5.6. Diagnostics

4. MARKET OVERVIEW
4.1. Chapter Overview
4.2. Deep Learning in Drug Discovery and Diagnostics: Market Landscape
4.3. Deep Learning in Drug Discovery
4.4. Deep Learning in Diagnostics
4.5. Deep Learning in Drug Discovery and Diagnostics
4.6. Deep Learning in Drug Discovery and Diagnostics: Non-Industry Players

5. COMPANY PROFILES
5.1. Chapter Overview
5.2. Advenio Technosys
5.3. AiCure
5.4. Atomwise
5.5. BenevolentAI
5.6. Butterfly Network
5.7. Enlitic
5.8. Human Longevity
5.9. InSilico Medicine
5.10. twoXAR
5.11. Zebra Medical Vision

6. CASE STUDY: IBM WATSON VERSUS GOOGLE DEEPMIND
6.1. Chapter Overview
6.2. IBM
6.3. Google
6.4. IBM v/s Google: Artificial Intelligence Acquisitions Portfolio
6.5. IBM v/s Google: Healthcare Partnerships and Collaborations
6.6. IBM v/s Google: Future Outlook and Primary Concerns

7. CAPITAL INVESTMENTS AND FUNDING
7.1. Chapter Overview
7.2. Deep Learning Market: Funding Instances
7.2.1. Funding Instances: Distribution by Year
7.2.2. Funding Instances: Distribution by Type of Funding
7.2.3. Leading Deep Learning Companies: Evaluation by Number of Funding Instances
7.2.4. Leading VC Firms / Investors: Evaluation by Number of Funding Instances

8. OPPORTUNITY ANALYSIS
8.1. Chapter Overview
8.2. Opportunity for Deep Learning in Drug Discovery
8.3. Opportunity for Deep Learning in Diagnostics 8.4. Overall Deep Learning Market in Drug Discovery and Diagnostics, 2017-2035

9. COMPANY VALUATION ANALYSIS
9.1. Chapter Overview
9.2. Company Valuation: Methodology
9.3. Company Valuation: Categorization by Multiple Parameters

10. DEEP LEARNING IN HEALTHCARE: EXPERT INSIGHTS
10.1. Chapter Overview
10.2. Industry Experts
10.2.1. Alex Jaimes, CTO, AiCure
10.2.2. Jeremy Howard, Founder, Enlitic
10.2.3. Riley Doyle, CEO, Desktop Genomics
10.3. University and Hospital Experts
10.3.1. Dr. Steven Alberts, Chairman of Medical Oncology, Mayo Clinic
10.3.2. Neil Lawrence, Professor, University of Sheffield
10.3.3. Yoshua Bengio, Professor, Université de Montréal
10.4. Venture Capital Experts
10.4.1. Robert Perl, CEO, Permanente Medical Group; Vinod Khosla, CEO, Khosla Ventures; Abraham Verghese, Professor, Stanford School of Medicine
10.5. Other Expert Opinions

11. CONCLUSION
11.1. Big Data and Deep Learning are Touted as the Next Big Thing in Digital Healthcare
11.2. The Field is Witnessing Rising Interest from Technology and Pharmaceutical Giants
11.3. Drug Discovery and Diagnostics have Emerged as the Major Application Areas for Deep Learning in Healthcare
11.4. Start-ups, Backed by Venture Capital Investors, are Driving Innovation in the Market
11.5. The Applications of Deep Learning are Expected to Result in Significant Time and Cost Savings
11.6. Data Sharing and Security Pose the Biggest Hurdles to the Implementation of Deep Learning Solutions
11.7. Certain Regulatory and Socio-Economic Concerns have Emerged as Additional Roadblocks in this Domain

12. INTERVIEW TRANSCRIPTS
12.1. Mausumi Acharya, CEO, Advenio Technosys
12.2. Carla Leibowitz, Head of Strategy and Marketing, Arterys
12.3. Deekshith Marla, CTO, Arya.ai and Sanjay Bhadra, COO, Arya.ai

13. APPENDIX 1: TABULATED DATA

14. APPENDIX 2: LIST OF COMPANIES AND ORGANIZATIONS

For more information about this report visit http://www.researchandmarkets.com/research/w98ksr/deep_learning_in

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         Related Topics: Diagnostics, Drug Discovery, Artificial Intelligence , Machine Learning and Data Mining