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Global Machine Learning in the Life Sciences Market to reach USD XX billion by the end of 2030

Global Machine Learning in the Life Sciences Market Size study & Forecast, by Component (Software, Services), by Application (Drug Discovery and Development, Precision Medicine, Genomics and Proteomics, Medical Imaging and Diagnostics, Clinical Research and Trials), by End User (Pharmaceutical and Biotechnology Companies, Academic and Research Institutions, Healthcare Providers, Contract Research Organizations (CROs)) and Regional Analysis, 2023-2030

Product Code: HLSB-79675777
Publish Date: 27-06-2023
Page: 200

Global Machine Learning in the Life Sciences Market is valued approximately USD XX billion in 2022 and is anticipated to grow with a healthy growth rate of more than XX% over the forecast period 2023-2030. The global machine learning in the life sciences market refers to the application of machine learning techniques and algorithms in various areas of the life sciences industry. Machine learning involves the use of computer algorithms that can learn from and make predictions or decisions based on patterns and data, without being explicitly programmed. The global machine learning in the life sciences market is influenced by factors such as advancements in AI and machine learning technologies, increasing availability of large-scale biological and clinical datasets, growing demand for personalized medicine. Moreover, the need for efficient drug discovery and development processes and rising initatives by key market players is creating lucrative growth opportunity for the market over the forecast period 2023-2030.

India is witnessing market growth as the software’s adoption is increase with growing digitalization in healthcare by the region. For instance, according to the Indian Society for Clinical Research (ISCR), a report was published in 2021 which stated that the digital adoption of clinical trials is witnessing growth for the market. Along with this, the government is supporting the healthcare industry in the country which is driving the growth of the market. As owing to this support, the companies can develop new and advanced technology for the proper management of patient’s data. For instance, in June 2021, the Indian government is planning to introduce a USD 6.8 billion worth credit incentive program in order to boost the country’s healthcare infrastructure. Along with this, the research and development activities for the clinical trial is increasing in Japan which is driving the growth for the market. For instance, in September 2020, The Medical Research Council (MRC) and Japan Agency for Medical Research and Development (AMED) have joined forces in order to support eight new regenerative medicine research partnerships. In this collaboration, MRC and AMED agreed to make almost USD 7.95 million available for supporting the collaborative projects that seek to advance regenerative approaches towards clinical use. However, the high cost of Machine Learning in the Life Sciences stifles market growth throughout the forecast period of 2023-2030.

The key regions considered for the Global Machine Learning in the Life Sciences Market study includes Asia Pacific, North America, Europe, Latin America, and Middle East & Africa. North America, particularly the United States, is a leading region in the machine learning in the life sciences market. The presence of major technology companies, research institutions, and pharmaceutical companies contributes to the growth of the market. The region has a well-established healthcare system, advanced research infrastructure, and supportive government initiatives promoting AI and machine learning applications in the life sciences sector. The Asia Pacific region is witnessing significant growth in the machine learning in the life sciences market. Countries such as China, Japan, and India are investing in AI and machine learning technologies to advance their healthcare systems and support research activities. The region has a large population, increasing healthcare expenditure, and a growing focus on precision medicine and personalized healthcare, driving the adoption of machine learning in the life sciences sector. Governments and industry players in the region are actively promoting AI and machine learning in healthcare and life sciences through policies, collaborations, and research initiatives.

Major market player included in this report are:
IBM Corporation
Microsoft Corporation
Alphabet Inc. (Google)
NVIDIA Corporation
Amazon Web Services (AWS)
Intel Corporation
Medtronic plc
Johnson & Johnson Services, Inc.
Koninklijke Philips N.V.
Roche Holding AG

Recent Developments in the Market:
Ø In February 2020, IBM Watson Health announced a collaboration with Pfizer to use machine learning to accelerate drug discovery in immunology and oncology.
Ø In September 2021, Verily launched the Project Baseline Health System Consortium, which aims to leverage machine learning to generate insights for personalized health management.
Ø In December 2020, Microsoft Research collaborated with biotech company Adaptive Biotechnologies to use machine learning for decoding the human immune system and developing personalized diagnostics and therapeutics.

Global Machine Learning in the Life Sciences Market Report Scope:
ü Historical Data – 2020 – 2021
ü Base Year for Estimation – 2022
ü Forecast period – 2023-2030
ü Report Coverage – Revenue forecast, Company Ranking, Competitive Landscape, Growth factors, and Trends
ü Segments Covered – Component, Application, End User, Region
ü Regional Scope – North America; Europe; Asia Pacific; Latin America; Middle East & Africa
ü Customization Scope – Free report customization (equivalent up to 8 analyst’s working hours) with purchase. Addition or alteration to country, regional & segment scope*

The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values to the coming years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within countries involved in the study.

The report also caters detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, it also incorporates potential opportunities in micro markets for stakeholders to invest along with the detailed analysis of competitive landscape and Component offerings of key players. The detailed segments and sub-segment of the market are explained below:

By Component:
Software
Services
By Application:
Drug Discovery and Development
Precision Medicine
Genomics and Proteomics
Medical Imaging and Diagnostics
Clinical Research and Trials
By End User:
Pharmaceutical and Biotechnology Companies
Academic and Research Institutions
Healthcare Providers
Contract Research Organizations (CROs)

By Region:

North America
U.S.
Canada

Europe
UK
Germany
France
Spain
Italy
ROE

Asia Pacific
China
India
Japan
Australia
South Korea
RoAPAC

Latin America
Brazil
Mexico

Middle East & Africa
Saudi Arabia
South Africa
Rest of Middle East & Africa

Chapter 1. Executive Summary
1.1. Market Snapshot
1.2. Global & Segmental Market Estimates & Forecasts, 2020-2030 (USD Billion)
1.2.1. Machine Learning in the Life Sciences Market, by Region, 2020-2030 (USD Billion)
1.2.2. Machine Learning in the Life Sciences Market, by Component, 2020-2030 (USD Billion)
1.2.3. Machine Learning in the Life Sciences Market, by Application, 2020-2030 (USD Billion)
1.2.4. Machine Learning in the Life Sciences Market, by End User, 2020-2030 (USD Billion)
1.3. Key Trends
1.4. Estimation Methodology
1.5. Research Assumption
Chapter 2. Global Machine Learning in the Life Sciences Market Definition and Scope
2.1. Objective of the Study
2.2. Market Definition & Scope
2.2.1. Industry Evolution
2.2.2. Scope of the Study
2.3. Years Considered for the Study
2.4. Currency Conversion Rates
Chapter 3. Global Machine Learning in the Life Sciences Market Dynamics
3.1. Machine Learning in the Life Sciences Market Impact Analysis (2020-2030)
3.1.1. Market Drivers
3.1.1.1. Advancements in AI and machine learning technologies
3.1.1.2. Increasing availability of large-scale biological and clinical datasets
3.1.1.3. Growing demand for personalized medicine
3.1.2. Market Challenges
3.1.2.1. High Cost of Machine Learning in the Life Sciences
3.1.3. Market Opportunities
3.1.3.1. Rising need for efficient drug discovery
3.1.3.2. Growing initiatives by key market players
Chapter 4. Global Machine Learning in the Life Sciences Market Industry Analysis
4.1. Porter’s 5 Force Model
4.1.1. Bargaining Power of Suppliers
4.1.2. Bargaining Power of Buyers
4.1.3. Threat of New Entrants
4.1.4. Threat of Substitutes
4.1.5. Competitive Rivalry
4.2. Porter’s 5 Force Impact Analysis
4.3. PEST Analysis
4.3.1. Political
4.3.2. Economical
4.3.3. Social
4.3.4. Technological
4.3.5. Environmental
4.3.6. Legal
4.4. Top investment opportunity
4.5. Top winning strategies
4.6. COVID-19 Impact Analysis
4.7. Disruptive Trends
4.8. Industry Expert Perspective
4.9. Analyst Recommendation & Conclusion
Chapter 5. Global Machine Learning in the Life Sciences Market, by Component
5.1. Market Snapshot
5.2. Global Machine Learning in the Life Sciences Market by Component, Performance – Potential Analysis
5.3. Global Machine Learning in the Life Sciences Market Estimates & Forecasts by Component 2020-2030 (USD Billion)
5.4. Machine Learning in the Life Sciences Market, Sub Segment Analysis
5.4.1. Software
5.4.2. Services
Chapter 6. Global Machine Learning in the Life Sciences Market, by Application
6.1. Market Snapshot
6.2. Global Machine Learning in the Life Sciences Market by Application, Performance – Potential Analysis
6.3. Global Machine Learning in the Life Sciences Market Estimates & Forecasts by Application 2020-2030 (USD Billion)
6.4. Machine Learning in the Life Sciences Market, Sub Segment Analysis
6.4.1. Drug Discovery and Development
6.4.2. Precision Medicine
6.4.3. Genomics and Proteomics
6.4.4. Medical Imaging and Diagnostics
6.4.5. Clinical Research and Trials
Chapter 7. Global Machine Learning in the Life Sciences Market, by End User
7.1. Market Snapshot
7.2. Global Machine Learning in the Life Sciences Market by End User, Performance – Potential Analysis
7.3. Global Machine Learning in the Life Sciences Market Estimates & Forecasts by End User 2020-2030 (USD Billion)
7.4. Machine Learning in the Life Sciences Market, Sub Segment Analysis
7.4.1. Pharmaceutical and Biotechnology Companies
7.4.2. Academic and Research Institutions
7.4.3. Healthcare Providers
7.4.4. Contract Research Organizations (CROs)
Chapter 8. Global Machine Learning in the Life Sciences Market, Regional Analysis
8.1. Top Leading Countries
8.2. Top Emerging Countries
8.3. Machine Learning in the Life Sciences Market, Regional Market Snapshot
8.4. North America Machine Learning in the Life Sciences Market
8.4.1. U.S. Machine Learning in the Life Sciences Market
8.4.1.1. Component breakdown estimates & forecasts, 2020-2030
8.4.1.2. Application breakdown estimates & forecasts, 2020-2030
8.4.1.3. End User breakdown estimates & forecasts, 2020-2030
8.4.2. Canada Machine Learning in the Life Sciences Market
8.5. Europe Machine Learning in the Life Sciences Market Snapshot
8.5.1. U.K. Machine Learning in the Life Sciences Market
8.5.2. Germany Machine Learning in the Life Sciences Market
8.5.3. France Machine Learning in the Life Sciences Market
8.5.4. Spain Machine Learning in the Life Sciences Market
8.5.5. Italy Machine Learning in the Life Sciences Market
8.5.6. Rest of Europe Machine Learning in the Life Sciences Market
8.6. Asia-Pacific Machine Learning in the Life Sciences Market Snapshot
8.6.1. China Machine Learning in the Life Sciences Market
8.6.2. India Machine Learning in the Life Sciences Market
8.6.3. Japan Machine Learning in the Life Sciences Market
8.6.4. Australia Machine Learning in the Life Sciences Market
8.6.5. South Korea Machine Learning in the Life Sciences Market
8.6.6. Rest of Asia Pacific Machine Learning in the Life Sciences Market
8.7. Latin America Machine Learning in the Life Sciences Market Snapshot
8.7.1. Brazil Machine Learning in the Life Sciences Market
8.7.2. Mexico Machine Learning in the Life Sciences Market
8.8. Middle East & Africa Machine Learning in the Life Sciences Market
8.8.1. Saudi Arabia Machine Learning in the Life Sciences Market
8.8.2. South Africa Machine Learning in the Life Sciences Market
8.8.3. Rest of Middle East & Africa Machine Learning in the Life Sciences Market

Chapter 9. Competitive Intelligence
9.1. Key Company SWOT Analysis
9.1.1. Company 1
9.1.2. Company 2
9.1.3. Company 3
9.2. Top Market Strategies
9.3. Company Profiles
9.3.1. IBM Corporation
9.3.1.1. Key Information
9.3.1.2. Overview
9.3.1.3. Financial (Subject to Data Availability)
9.3.1.4. Product Summary
9.3.1.5. Recent Developments
9.3.2. Microsoft Corporation
9.3.3. Alphabet Inc. (Google)
9.3.4. NVIDIA Corporation
9.3.5. Amazon Web Services (AWS)
9.3.6. Intel Corporation
9.3.7. Medtronic plc
9.3.8. Johnson & Johnson Services, Inc.
9.3.9. Koninklijke Philips N.V.
9.3.10. Roche Holding AG
Chapter 10. Research Process
10.1. Research Process
10.1.1. Data Mining
10.1.2. Analysis
10.1.3. Market Estimation
10.1.4. Validation
10.1.5. Publishing
10.2. Research Attributes
10.3. Research Assumption

At Bizwit Research and Consultancy, we employ a thorough and iterative research methodology with the goal of minimizing discrepancies, ensuring the provision of highly accurate estimates and predictions over the forecast period. Our approach involves a combination of bottom-up and top-down strategies to effectively segment and estimate quantitative aspects of the market, utilizing our proprietary data & AI tools. Our Proprietary Tools allow us for the creation of customized models specific to the research objectives. This enables us to develop tailored statistical models and forecasting algorithms to estimate market trends, future growth, or consumer behavior. The customization enhances the accuracy and relevance of the research findings.
We are dedicated to clearly communicating the purpose and objectives of each research project in the final deliverables. Our process begins by identifying the specific problem or challenge our client wishes to address, and from there, we establish precise research questions that need to be answered. To gain a comprehensive understanding of the subject matter and identify the most relevant trends and best practices, we conduct an extensive review of existing literature, industry reports, case studies, and pertinent academic research.
Critical elements of methodology employed for all our studies include:
Data Collection:
To determine the appropriate methods of data collection based on the research objectives, we consider both primary and secondary sources. Primary data collection involves gathering information directly from various industry experts in core and related fields, original equipment manufacturers (OEMs), vendors, suppliers, technology developers, alliances, and organizations. These sources encompass all segments of the value chain within the specific industry. Through in-depth interviews, we engage with key industry participants, subject-matter experts, C-level executives of major market players, industry consultants, and other relevant experts. This allows us to obtain and validate critical qualitative and quantitative information while evaluating market prospects. AI and Big Data are instrumental in our primary research, providing us with powerful tools to collect, analyze, and derive insights from data efficiently. These technologies contribute to the advancement of research methodologies, enabling us to make data-driven decisions and uncover valuable findings.
In addition to primary sources, we extensively utilize secondary sources to enhance our research. These include directories, databases, journals focusing on related industries, company newsletters, and information portals such as Bloomberg, D&B Hoovers, and Factiva. These secondary sources enable us to identify and gather valuable information for our comprehensive, technical, market-oriented, and commercial study of the market. Additionally, we utilize AI algorithms to automate the collection of vast amounts of data from various sources such as surveys, social media platforms, online transactions, and web scraping. And employ Big Data technologies for storage and processing of large datasets, ensuring that no valuable information is missed during the data collection process.
Data Analysis:
Our team of experts carefully examine the gathered data using suitable statistical techniques and qualitative analysis methods. For quantitative analysis, we employ descriptive statistics, regression analysis, and other advanced statistical methods, depending on the characteristics of the data. This analysis may also incorporate the utilization of AI tools and big data analysis techniques to extract meaningful insights.
To ensure the accuracy and reliability of our findings, we extensively leverage data science techniques, which help us minimize discrepancies and uncertainties in our analysis. We employ Data Science to clean and preprocess the data, ensuring its quality and reliability. This involves handling missing data, removing outliers, standardizing variables, and transforming data into suitable formats for analysis. The application of data science techniques enhances our accuracy, efficiency, and depth of analysis, enabling us to stay competitive in dynamic market environments.
Market Size Estimation:
Our proprietary data tools play a crucial role in deriving our market estimates and forecasts. Each study involves the creation of a unique and customized model. The model incorporates the gathered information on market dynamics, technology landscape, application development, and pricing trends. AI techniques, such as machine learning and deep learning, aid us to analyze patterns within the data to identify correlations, trends, and relationships. By recognizing patterns in consumer behavior, purchasing habits, or market dynamics, our AI algorithms aid us in more precise estimations of market size. These factors are simultaneously analyzed within the model, allowing for a comprehensive assessment. To quantify their impact over the forecast period, correlation, regression, and time series analysis are employed.
To estimate and validate the market size, we employ both top-down and bottom-up approaches. The preference is given to a bottom-up approach, where key regional markets are analyzed as separate entities. This data is then integrated to obtain global estimates. This approach is crucial as it provides a deep understanding of the industry and helps minimize errors.
In our forecasting process, we consider various parameters such as economic tools, technological analysis, industry experience, and domain expertise. By taking all these factors into account, we strive to produce accurate and reliable market forecasts. When forecasting, we take into consideration several parameters, which include:
Market driving trends and favorable economic conditions
Restraints and challenges that are expected to be encountered during the forecast period.
Anticipated opportunities for growth and development
Technological advancements and projected developments in the market
Consumer spending trends and dynamics
Shifts in consumer preferences and behaviors.
The current state of raw materials and trends in supply versus pricing
Regulatory landscape and expected changes or developments.
The existing capacity in the market and any expected additions or expansions up to the end of the forecast period.
To assess the market impact of these parameters, we assign weights to each one and utilize weighted average analysis. This process allows us to quantify their influence on the market and derive an expected growth rate for the forecasted period. By considering these various factors and applying a weighted analysis approach, we strive to provide accurate and reliable market forecasts.
Insight Generation & Report Presentation:
After conducting the research, our experts analyze the findings in relation to the research objectives and the specific needs of the client. They generate valuable insights and recommendations that directly address the client’s business challenges. These insights are carefully connected to the research findings to provide a comprehensive understanding.
Next, we create a well-structured research report that effectively communicates the research findings, insights, and recommendations to the client. To enhance clarity and comprehension, we utilize visual aids such as charts, graphs, and tables. These visual elements are employed to present the data in an engaging and easily understandable format, ensuring that the information is accessible and visually appealing to the client. Our aim is to deliver a clear and concise report that conveys the research findings effectively and provides actionable recommendations to meet the client’s specific needs.

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