Artificial Intelligence in Healthcare-Thematic Research
Machine learning is an Artificial Intelligence (AI) technology that allows machines to learn by using algorithms to interpret data from connected things to predict outcomes and learn from successes and failures.There are many other AI technologies-from image recognition to natural language processing, gesture control, context awareness, and predictive application programming interfaces (APIs)-but machine learning is where most of the investment community's funding has flowed in recent years. It is also the technology most likely to allow machines to ultimately surpass the intelligence levels of humans.
Artificial Intelligence (AI) can significantly transform how healthcare systems operate, connect with patients, and provide care. Artificial Intelligence (AI) is the next step following the implementation of sophisticated big data & analytics (BDA) approaches, and can unlock large volumes of data if it can be accessed in an automated way in real-time without any human effort. Healthcare companies who implement Artificial Intelligence (AI) are entering a new era that is anticipated to revolutionize the industry by making various tasks more efficient, such as drug discovery, clinical trials, robotic surgery, and patient management.
For instance, pharmaceutical giant Merck is working with Atomwise, the creator of AtomNet, which uses deep learning technology for the discovery of novel small molecules. Although the project is confidential, Merck is leveraging Atomwise's AI-based technology to scan existing medicines that could be redesigned to fight old and upcoming diseases. This particular program found two medications to reduce Ebola infectivity in one day, which if done in a traditional method would have taken months or years.
The report "Artificial Intelligence in Healthcare-Thematic Research", is part of an ecosystem of thematic investment research reports.The report offers in-depth research intoArtificial Intelligence; identifying winners and losers based on technology leadership, market position and other factors.
Companies Mentioned: 3M Healthcare (Medical Devices), Abbott Laboratories, AbbVie, Amgen, AstraZeneca, B. Braun (Medical Devices), Bayer AG, Becton Dickinson, Biogen, Biotronik SE, Boehringer-Ingelheim, Boston Scientific, Bristol-Myers Squibb, Cardinal Health, CareFusion (Medical Devices), Celgene Corporation, Eli Lilly, GE Healthcare, Getinge Group, Gilead Sciences, GSK, Hologic, Johnson & Johnson, Medtronic, Merck, Novartis, Novo Nordisk, Olympus Corporation, Pfizer, Philips Healthcare, Roche, Sanofi, Shire, Siemens (Healthineers), Smith & Nephew, Stryker, Takeda, Terumo, Teva Pharmaceuticals, Zimmer Biomet Holdings, Alphabet, Amazon, Apple, Baidu, Cognex, Facebook, IBM, iFlytek, Intel, Nvidia, Salesforce, SAP, Splunk, Darktrace, Descartes Labs, Mobvoi, Palantir, Sentient Technologies, Vicarious, Ayasdi, Blue Yonder, IPsoft, Kensho, DataFox, Numenta, Preferred Networks, Quid, Zephyr Health
The focus of this report is on the applications of AI in healthcare, 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
Reasons to buy
The report identifies-
- Key technology leaders in each of the 10 key AI technologies, including machine learning, smart robots, image recognition, video recognition, recommendation engines, NLP, virtual personal assistants, gesture control, context awareness, and predictive APIs.
- Key trends we expect to see in the AI sector over the next two to three years categorised as technology trends, macro-economic trends, and applications of AI in healthcare.
- The value chain across the 10 categories of AI, along with an industry analysis which includes the tech sector's angle, enterprise software players, proprietary datasets, the transformation of the Chipset market, timeline, and partnerships and acquisitions.
- The impact of AI on healthcare with the help of healthcare case studies.
- A technology briefing on the histroy of ML, and how deep learning works.
Table of Contents
Technology Trends 5
Macro-Economic Trends 7
Applications of AI in Healthcare 8
VALUE CHAIN 10
Ten Categories of AI 11
Machine Learning 11
Smart Robots 12
Image Recognition 13
Video Recognition 14
Recommendation Engines 14
Natural Language Processing 15
Virtual Personal Assistants 15
Gesture Control 16
Context Aware Computing 17
Predictive APIs 17
INDUSTRY ANALYSIS 18
The Tech Sector's Angle 18
Enterprise Software Players 19
Proprietary Datasets Are Also Important 19
AI and Machine Learning Are Transforming the Chipset Market 20
The Two Critical Components of Any Successful AI Engine 20
M&A and Partnerships 25
IMPACT OF AI ON HEALTHCARE 27
Healthcare Case Studies 27
COMPANIES SECTION 29
Listed Tech Companies 29
Privately Held Tech Companies 32
Healthcare Companies 34
TECHNOLOGY BRIEFING 40
History of Machine Learning 40
How Does Deep Learning Work? 40
APPENDIX: OUR THEMATIC RESEARCH METHODOLOGY 43
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