Artificial Intelligence in Healthcare – Thematic Research
All the vital news, analysis, and commentary curated by our industry experts.
For six decades, machine learning was poised to take off because members of the “artificial intelligentsia” had already come up with the theoretical models that could make it work. The problem was for a lack of rich data sets and affordable accelerated computing technology to ignite it. These are now becoming more available.
Now, amid a swirl of hype, machine learning —software that becomes smarter as it trains itself on large amounts of data—is going mainstream, and within five years its deployment will be essential to the survival of companies of all shapes and sizes across all sectors.
Many companies, like Alphabet, have already become “AI-first” companies, with machine learning at their cores. At the same time, many machine learning techniques are getting commoditized by being open-sourced and pre-packaged into developer toolkits that anyone can use.
The applications of AI in healthcare are numerous, with the potential to transform key aspects of the industry such as data management, drug discovery, personalized and precision medicine, clinical trial design, and management, and robotic surgery.
GlobalData’s Thematic Research – Artificial Intelligence in Healthcare provides in-house analyst expertise on the impact of the applications of AI in healthcare and identifies the winners in each of the 10 key AI technologies.
Components of the report include:
Key Players — identify the big players in the AI sector and where they sit in the value chain.
Trends in the AI Sector— key trends driving the AI sector classified into three key categories: technology trends, macro-economic trends, and healthcare industry applications.
Industry Analysis — impact of adoption of AI beyond the technology sector and identification of the key components of a successful AI engine.
Impact of AI in Healthcare — key case studies demonstrating how healthcare companies are implementing AI-based solutions in the healthcare industry.
Value Chain – identify the 10 categories of AI driving growth in the AI sector, highlighting the leaders in each category.
Reasons to Buy
Develop and design your corporate strategies through an in-house expert analysis of AI-based solutions impacting the healthcare industry.
Develop business strategies by understanding the key AI technologies being used in the healthcare industry.
Stay up to date on the industry’s big players in the AI sector and where they sit in the value chain.
Identify emerging industry trends in AI technologies to gain a competitive advantage.
Table of Contents
1 Artificial Intelligence in Healthcare
1.1 For many investors, machine learning = AI
1.2 Tech leaders
1.3 Healthcare leaders
1.5 Related reports
1.6 Report type
2 Table of Contents
2.1 List of Tables
2.2 List of Figures
4.1 Technology Trends
4.2 Macro-Economic Trends
4.3 Applications of AI in Healthcare
5 Value Chain
5.1 Ten Categories of AI
5.1.1 Machine Learning
5.1.2 Smart Robots
5.1.3 Image Recognition
5.1.4 Video Recognition
5.1.5 Recommendation Engines 16
5.1.6 Natural Language Processing
5.1.7 Virtual Personal Assistants
5.1.8 Gesture Control
5.1.9 Context Aware Computing
5.1.10 Predictive APIs
6 Industry Analysis
6.1 The Tech Sector’s Angle
6.1.1 The Web-Scale Companies
6.1.2 Enterprise Software Players
6.1.3 Proprietary Datasets Are Also Important
6.1.4 AI and Machine Learning Are Transforming the Chipset Market
6.1.5 The Two Critical Components of Any Successful AI Engine
6.1.7 M&A and Partnerships
7 Impact of AI on Healthcare
7.1 Healthcare Case Studies
7.1.1 Merck – AI for Drug Discovery
7.1.2 Stryker – AI-Based Technology for Robotic Surgery
7.1.3 Roche – Machine Learning for Large-Scale Genome Sequencing
7.1.4 J&J – AI for Clinical Trial Data Analysis
7.1.5 Prognos – AI to Drive Earlier Healthcare Decisions
7.1.6 Novartis – AI to Drive Personalized and Precision Medicine
8 Companies Section
8.1 Listed Tech Companies
8.2 Privately Held Tech Companies
8.3 Healthcare Companies
9 Technology Briefing
9.2 History of Machine Learning
9.3 How Does Deep Learning Work?
10 Appendix: Our “Thematic” research methodology
10.1 Traditional thematic research does a poor job of picking winners and losers
10.2 Introducing GlobalData’s thematic engine
10.3 This is how it works
10.4 How our research reports fit into our overall research methodology
10.5 About GlobalData
10.6 Contact us
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