Dublin, October 12, 2022 /PRNewswire/ — “Global Market for Machine Learning in Life Sciences” report added of ResearchAndMarkets.com Recruitment.
The report highlights current and future market potential for machine learning in life sciences and provides an in-depth analysis of the competitive landscape, regulatory scenarios, drivers, constraints, opportunities, and trends in the market. The report also covers market forecasts from 2022 to 2027 and presents key market players.
The publisher analyzes each technology in detail, determines key players and current market conditions, and presents growth forecasts for the next five years. Scientific challenges and advances are highlighted, including the latest trends. A global view of government regulations, key collaborations, recent patents, and factors impacting the industry.
Leading machine learning technologies and products in life sciences are analyzed to determine current and future market conditions and projected growth from 2022 to 2027. sponsored.
Artificial Intelligence (AI) To identify the scientific branch that covers computer hardware and software intended to create machines (such as robots) and to replicate the intellectual behavior of humans in whole or in part. terminology used. AI, considered a branch of cognitive computing, is a term that refers to systems that can learn, reason, and interact with humans. Cognitive computing is a combination of computer science and cognitive science.
ML algorithms are designed to perform tasks such as browsing data, extracting information relevant to a range of tasks, discovering rules governing data, making decisions and predictions, and following specific instructions. . As an example, ML is used in image recognition to identify image content after a machine has been instructed to learn the differences between images of many different categories.
There are several types of ML algorithms. The most common are nearest neighbors, naive Bayes, decision trees, a priori algorithms, linear regression, case-based inference, hidden Markov models, support vector machines (SVM), clustering, and artificial algorithms. neural network. Artificial Neural Networks (ANNs) have gained great popularity in high-level computing in recent years.
They are modeled to function similarly to the human brain. The most basic type of ANN is a feedforward network. It is formed by an input layer, a hidden layer, and an output layer, where data moves in one direction from the input layer to the output layer and is transformed in the hidden layer.
What is included in the report
32 data tables and 28 additional tables
A comprehensive overview and up-to-date analysis of the global market for machine learning in the life sciences industry
Analysis of global market trends, including historical market revenue data for 2020 and 2021, estimates for 2022, and compound annual growth rate (CAGR) forecasts to 2027
Highlights of the current and future market potential of ML in life sciences applications and areas of focus to forecast this market into various segments and sub-segments
Estimation of actual market size (in USD million) for machine learning in life sciences and corresponding market share analysis based on solution offering, deployment mode, application and geographic region
An update on key market drivers and opportunities, industry changes and regulations, and other demographic factors that will affect demand for this market in the coming years (2022-2027)
Discusses viable technology drivers through a holistic review of various platform technologies for new and existing applications of machine learning in life sciences.
Identification of key stakeholders and analysis of the competitive landscape based on recent developments and segment revenues
It focuses on key growth strategies, product launches, major acquisitions, and competitive benchmarking adopted by the global machine learning leading players in the life sciences market.
Profile descriptions of key market players such as Alteryx Inc., Canon Medical Systems Corp., Hewlett Packard Enterprise (HPE), KNIME AG, Microsoft Corp., Phillips Healthcare
Chapter 1; Introduction
Chapter 2. Summary and Highlights
Chapter 3 Market Overview
3.1.1 Understanding artificial intelligence in healthcare
3.1.2 Artificial intelligence in healthcare evolution and transition
Chapter 4 Impact of the Covid-19 Pandemic
4.1.1 Market Impact of Covid-19
Chapter 5 Market Dynamics
5.1 Market Driver
5.1.1 Investment in Ai Health sector
5.1.2 Increase in chronic diseases
5.1.3 Highly Accurate Results
5.1.4 Increase in R&D budget
5.2 Market Constraints and Challenges
5.2.1 Reluctance of healthcare workers to adopt AI-based technologies
5.2.2 User Data Privacy and Security
5.2.3 Hackers and machine learning
5.2.4 Vague regulatory guidelines for medical software
5.3 Market Opportunities
5.3.1 Untapped Potential in Emerging Markets
5.4 Value chain analysis
Chapter 6 Market Breakdown by Offering
6.1.1 Market size and forecast
6.2.1 Market size and forecast
7. Market Breakdown by Deployment Mode
7.1.1 Market size and forecast
7.2.1 Market size and forecast
Chapter 8 Market Breakdown by Application
8.1.1 Market size and forecast
8.2.1 Market size and forecast
8.3 Healthcare management
8.3.1 Market size and forecast
Chapter 9 Regional Market Breakdown
9.1 Global Market
9.2 North America
9.2.1 United States
9.3.2 United Kingdom
9.3.6 Rest Europe
9.4 Asia Pacific
9.4.4 Rest Asia Pacific
9.5 Other countries
Chapter 10 Regulation and Finance
10.1 Regulatory framework
10.1.1 American Diabetes Association Diabetes Standards of Care
10.1.2 ATA Guidelines for Artificial Intelligence
10.1.3 India Ai Guidelines, Strategies and Standards
Chapter 11 Competitive Environment
11.1.4 Parent Market: Global Artificial Intelligence Market
Chapter 12 Company Profile
For more information on this report, please visit https://www.researchandmarkets.com/r/qc8qjo.
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