DUBLIN--(BUSINESS WIRE)--The "Global Markets for Machine Learning in the Life Sciences" report has been added to ResearchAndMarkets.com's offering.
This report highlights the current and future market potential for machine learning in life sciences and provides a detailed analysis of the competitive environment, regulatory scenario, drivers, restraints, opportunities and trends in the market. The report also covers market projections from 2022 through 2027 and profiles key market players.
Companies Mentioned
The publisher analyzes each technology in detail, determines major players and current market status, and presents forecasts of growth over the next five years. Scientific challenges and advances, including the latest trends, are highlighted. Government regulations, major collaborations, recent patents and factors affecting the industry from a global perspective are examined.
Key machine learning in life sciences technologies and products are analyzed to determine present and future market status, and growth is forecast from 2022 to 2027. An in-depth discussion of strategic alliances, industry structures, competitive dynamics, patents and market driving forces is also provided.
Artificial intelligence (AI) is a term used to identify a scientific field that covers the creation of machines (e.g., robots) as well as computer hardware and software aimed at reproducing wholly or in part the intelligent behavior of human beings. AI is considered a branch of cognitive computing, a term that refers to systems able to 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 data browsing, extracting information that is relevant to the scope of the task, discovering rules that govern the data, making decisions and predictions, and accomplishing specific instructions. As an example, ML is used in image recognition to identify the content of an image after the machine has been instructed to learn the differences among many different categories of images.
There are several types of ML algorithms, the most common of which are nearest neighbor, naive Bayes, decision trees, a priori algorithms, linear regression, case-based reasoning, hidden Markov models, support vector machines (SVMs), clustering, and artificial neural networks. Artificial neural networks (ANN) have achieved great popularity in recent years for high-level computing.
They are modeled to act similarly to the human brain. The most basic type of ANN is the feedforward network, which is formed by an input layer, a hidden layer and an output layer, with data moving in one direction from the input layer to the output layer, while being transformed in the hidden layer.
Report Includes
Key Topics Covered:
Chapter 1 Introduction
Chapter 2 Summary and Highlights
Chapter 3 Market Overview
3.1 Introduction
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 Introduction
4.1.1 Impact of Covid-19 on the Market
Chapter 5 Market Dynamics
5.1 Market Drivers
5.1.1 Investment in Ai Health Sector
5.1.2 Rising Chronic Diseases
5.1.3 Advanced, Precise Results
5.1.4 Increasing Research and Development Budget
5.2 Market Restraints and Challenges
5.2.1 Reluctance Among Medical Practitioners to Adopt Ai-Based Technologies
5.2.2 Privacy and Security of User Data
5.2.3 Hackers and Machine Learning
5.2.4 Ambiguous 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
Chapter 7 Market Breakdown by Deployment Mode
Chapter 8 Market Breakdown by Application
Chapter 9 Market Breakdown by Region
Chapter 10 Regulations and Finance
Chapter 11 Competitive Landscape
Chapter 12 Company Profiles
For more information about this report visit https://www.researchandmarkets.com/r/oqwcnh
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