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Published Date: 2024-10-17 | Pages: 116 | Tables: 183 | Service & Software
Machine learning in pharmaceutical industry refers to the use of artificial intelligence (AI) algorithms and statistical models to analyze large datasets in order to identify patterns and relationships that can be used to make better decisions related to drug development, clinical trials, and patient care.
The global market for Machine Learning in Pharmaceutical was estimated to be worth US$ 2314 million in 2023 and is forecast to a readjusted size of US$ 3481 million by 2030 with a CAGR of 6.0% during the forecast period 2024-2030
The global pharmaceutical market is 1475 billion USD in 2022, growing at a CAGR of 5% during the next six years. The pharmaceutical market includes chemical drugs and biological drugs. For biologics is expected to 381 billion USD in 2022. In comparison, the chemical drug market is estimated to increase from 1005 billion in 2018 to 1094 billion U.S. dollars in 2022. The pharmaceutical market factors such as increasing demand for healthcare, technological advancements, and the rising prevalence of chronic diseases, increase in funding from private & government organizations for development of pharmaceutical manufacturing segments and rise in R&D activities for drugs. However, the industry also faces challenges such as stringent regulations, high costs of research and development, and patent expirations. Companies need to continuously innovate and adapt to these challenges to stay competitive in the market and ensure their products reach patients in need. Additionally, the COVID-19 pandemic has highlighted the importance of vaccine development and supply chain management, further emphasizing the need for pharmaceutical companies to be agile and responsive to emerging public health needs.
Report Scope
This report aims to provide a comprehensive presentation of the global market for Machine Learning in Pharmaceutical, focusing on the total sales revenue, key companies market share and ranking, together with an analysis of Machine Learning in Pharmaceutical by region & country, by Type, and by Application.
The Machine Learning in Pharmaceutical market size, estimations, and forecasts are provided in terms of sales revenue ($ millions), considering 2023 as the base year, with history and forecast data for the period from 2019 to 2030. With both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding Machine Learning in Pharmaceutical.
Market Segmentation
By Company
Cyclica inc
BioSymetrics Inc.
Cloud Pharmaceuticals, Inc
Deep Genomics
Atomwise Inc.
Alphabet Inc.
NVIDIA Corporation
International Business Machines Corporation
Microsoft Corporation
IBM
Segment by Type:
Pharmaceutical
Clinical Use
Others
Segment by Application
Drug Development
Clinical Trials
Patient Care
Others
By Region
North America
United States
Canada
Europe
Germany
France
U.K.
Italy
Russia
Asia-Pacific
China
Japan
South Korea
China Taiwan
Southeast Asia
India
Latin America
Mexico
Brazil
Argentina
Middle East & Africa
Turkey
Saudi Arabia
UAE
Chapter Outline
Chapter 1: Introduces the report scope of the report, global total market size. This chapter also provides the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by manufacturers in the industry, and the analysis of relevant policies in the industry.
Chapter 2: Detailed analysis of Machine Learning in Pharmaceutical manufacturers competitive landscape, revenue market share, latest development plan, merger, and acquisition information, etc.
Chapter 3: Provides the analysis of various market segments by Type, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different market segments.
Chapter 4: Provides the analysis of various market segments by Application, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.
Chapter 5: Revenue of Machine Learning in Pharmaceutical in regional level. It provides a quantitative analysis of the market size and development potential of each region and introduces the market development, future development prospects, market space, and market size of each country in the world.
Chapter 6: Revenue of Machine Learning in Pharmaceutical in country level. It provides sigmate data by Type, and by Application for each country/region.
Chapter 7: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product revenue, gross margin, product introduction, recent development, etc.
Chapter 8: Analysis of industrial chain, including the upstream and downstream of the industry.
Chapter 9: Conclusion.
Research Methodology
The research methodology employed has been subjected by numerous procedures in order to guarantee the quality and accuracy of the data contained within the reports. The analysts are employed full-time and received more than six months training to satisfy the standard of our company. Our methodology can be divided into five stages:
★Stage 1 SECONDARY RESEARCH
The research team first collaborates with magazines, trade associations and administrative departments in the research field. The information provided by our internal documentation service is helpful for our further research. Our team has a wealth of experience and knowledge, and can effectively extract accurate information from existing resources.
★Stage 2 PRIMARY RESEARCH:INTERVIEWS WITH TRADE SOURCES
After the first stage, the research team conducts a large number of face-to-face or telephone interviews with representative companies working in the research field. Analysts are trying to have an opportunity to talk to leading companies and small companies in the field. Upstream suppliers, manufacturers, distributors, importers, installers, wholesalers and consumers were included in the interview. The data collected during the interview were then carefully examined and compared with the secondary study.
★Stage 3 ANALYSIS OF THE GATHERED DATA
The analysis team examines and synthesizes the data collected in the first two stages. In order to validate the data, a second round of interviews can be conducted.
★Stage 4 QUANTITATIVE DATA
The quantitative data such as market estimates, production and capacity of manufacturer, market forecasts and investment feasibility is provided by our company. The data is based on the estimates obtained during stage 3.
★Stage 5 QUALITY CONTROL
Before publishing, each report undergoes a rigorous review and editing process, which is done by the experience management team to ensure the reliability of the published data. Every analyst on the research team receives support and continuous training as part of our internal quality process.
1 Market Overview
1.1 Machine Learning in Pharmaceutical Product Introduction
1.2 Global Machine Learning in Pharmaceutical Market Size Forecast
1.3 Machine Learning in Pharmaceutical Market Trends & Drivers
1.3.1 Machine Learning in Pharmaceutical Industry Trends
1.3.2 Machine Learning in Pharmaceutical Market Drivers & Opportunity
1.3.3 Machine Learning in Pharmaceutical Market Challenges
1.3.4 Machine Learning in Pharmaceutical Market Restraints
1.4 Assumptions and Limitations
1.5 Study Objectives
1.6 Years Considered
2 Competitive Analysis by Company
2.1 Global Machine Learning in Pharmaceutical Players Revenue Ranking (2023)
2.2 Global Machine Learning in Pharmaceutical Revenue by Company (2019-2024)
2.3 Key Companies Machine Learning in Pharmaceutical Manufacturing Base Distribution and Headquarters
2.4 Key Companies Machine Learning in Pharmaceutical Product Offered
2.5 Key Companies Time to Begin Mass Production of Machine Learning in Pharmaceutical
2.6 Machine Learning in Pharmaceutical Market Competitive Analysis
2.6.1 Machine Learning in Pharmaceutical Market Concentration Rate (2019-2024)
2.6.2 Global 5 and 10 Largest Companies by Machine Learning in Pharmaceutical Revenue in 2023
2.6.3 Global Top Companies by Company Type (Tier 1, Tier 2, and Tier 3) & (based on the Revenue in Machine Learning in Pharmaceutical as of 2023)
2.7 Mergers & Acquisitions, Expansion
3 Segmentation by Type
3.1 Introduction by Type
3.1.1 Pharmaceutical
3.1.2 Clinical Use
3.1.3 Others
3.2 Global Machine Learning in Pharmaceutical Sales Value by Type
3.2.1 Global Machine Learning in Pharmaceutical Sales Value by Type (2019 VS 2023 VS 2030)
3.2.2 Global Machine Learning in Pharmaceutical Sales Value, by Type (2019-2030)
3.2.3 Global Machine Learning in Pharmaceutical Sales Value, by Type (%) (2019-2030)
4 Segmentation by Application
4.1 Introduction by Application
4.1.1 Drug Development
4.1.2 Clinical Trials
4.1.3 Patient Care
4.1.4 Others
4.2 Global Machine Learning in Pharmaceutical Sales Value by Application
4.2.1 Global Machine Learning in Pharmaceutical Sales Value by Application (2019 VS 2023 VS 2030)
4.2.2 Global Machine Learning in Pharmaceutical Sales Value, by Application (2019-2030)
4.2.3 Global Machine Learning in Pharmaceutical Sales Value, by Application (%) (2019-2030)
5 Segmentation by Region
5.1 Global Machine Learning in Pharmaceutical Sales Value by Region
5.1.1 Global Machine Learning in Pharmaceutical Sales Value by Region: 2019 VS 2023 VS 2030
5.1.2 Global Machine Learning in Pharmaceutical Sales Value by Region (2019-2024)
5.1.3 Global Machine Learning in Pharmaceutical Sales Value by Region (2025-2030)
5.1.4 Global Machine Learning in Pharmaceutical Sales Value by Region (%), (2019-2030)
5.2 North America
5.2.1 North America Machine Learning in Pharmaceutical Sales Value, 2019-2030
5.2.2 North America Machine Learning in Pharmaceutical Sales Value by Country (%), 2023 VS 2030
5.3 Europe
5.3.1 Europe Machine Learning in Pharmaceutical Sales Value, 2019-2030
5.3.2 Europe Machine Learning in Pharmaceutical Sales Value by Country (%), 2023 VS 2030
5.4 Asia Pacific
5.4.1 Asia Pacific Machine Learning in Pharmaceutical Sales Value, 2019-2030
5.4.2 Asia Pacific Machine Learning in Pharmaceutical Sales Value by Country (%), 2023 VS 2030
5.5 South America
5.5.1 South America Machine Learning in Pharmaceutical Sales Value, 2019-2030
5.5.2 South America Machine Learning in Pharmaceutical Sales Value by Country (%), 2023 VS 2030
5.6 Middle East & Africa
5.6.1 Middle East & Africa Machine Learning in Pharmaceutical Sales Value, 2019-2030
5.6.2 Middle East & Africa Machine Learning in Pharmaceutical Sales Value by Country (%), 2023 VS 2030
6 Segmentation by Key Countries/Regions
6.1 Key Countries/Regions Machine Learning in Pharmaceutical Sales Value Growth Trends, 2019 VS 2023 VS 2030
6.2 Key Countries/Regions Machine Learning in Pharmaceutical Sales Value
6.3 United States
6.3.1 United States Machine Learning in Pharmaceutical Sales Value, 2019-2030
6.3.2 United States Machine Learning in Pharmaceutical Sales Value by Type (%), 2023 VS 2030
6.3.3 United States Machine Learning in Pharmaceutical Sales Value by Application, 2023 VS 2030
6.4 Europe
6.4.1 Europe Machine Learning in Pharmaceutical Sales Value, 2019-2030
6.4.2 Europe Machine Learning in Pharmaceutical Sales Value by Type (%), 2023 VS 2030
6.4.3 Europe Machine Learning in Pharmaceutical Sales Value by Application, 2023 VS 2030
6.5 China
6.5.1 China Machine Learning in Pharmaceutical Sales Value, 2019-2030
6.5.2 China Machine Learning in Pharmaceutical Sales Value by Type (%), 2023 VS 2030
6.5.3 China Machine Learning in Pharmaceutical Sales Value by Application, 2023 VS 2030
6.6 Japan
6.6.1 Japan Machine Learning in Pharmaceutical Sales Value, 2019-2030
6.6.2 Japan Machine Learning in Pharmaceutical Sales Value by Type (%), 2023 VS 2030
6.6.3 Japan Machine Learning in Pharmaceutical Sales Value by Application, 2023 VS 2030
6.7 South Korea
6.7.1 South Korea Machine Learning in Pharmaceutical Sales Value, 2019-2030
6.7.2 South Korea Machine Learning in Pharmaceutical Sales Value by Type (%), 2023 VS 2030
6.7.3 South Korea Machine Learning in Pharmaceutical Sales Value by Application, 2023 VS 2030
6.8 Southeast Asia
6.8.1 Southeast Asia Machine Learning in Pharmaceutical Sales Value, 2019-2030
6.8.2 Southeast Asia Machine Learning in Pharmaceutical Sales Value by Type (%), 2023 VS 2030
6.8.3 Southeast Asia Machine Learning in Pharmaceutical Sales Value by Application, 2023 VS 2030
6.9 India
6.9.1 India Machine Learning in Pharmaceutical Sales Value, 2019-2030
6.9.2 India Machine Learning in Pharmaceutical Sales Value by Type (%), 2023 VS 2030
6.9.3 India Machine Learning in Pharmaceutical Sales Value by Application, 2023 VS 2030
7 Company Profiles
7.1 Cyclica inc
7.1.1 Cyclica inc Profile
7.1.2 Cyclica inc Main Business
7.1.3 Cyclica inc Machine Learning in Pharmaceutical Products, Services and Solutions
7.1.4 Cyclica inc Machine Learning in Pharmaceutical Revenue (US$ Million) & (2019-2024)
7.1.5 Cyclica inc Recent Developments
7.2 BioSymetrics Inc.
7.2.1 BioSymetrics Inc. Profile
7.2.2 BioSymetrics Inc. Main Business
7.2.3 BioSymetrics Inc. Machine Learning in Pharmaceutical Products, Services and Solutions
7.2.4 BioSymetrics Inc. Machine Learning in Pharmaceutical Revenue (US$ Million) & (2019-2024)
7.2.5 BioSymetrics Inc. Recent Developments
7.3 Cloud Pharmaceuticals, Inc
7.3.1 Cloud Pharmaceuticals, Inc Profile
7.3.2 Cloud Pharmaceuticals, Inc Main Business
7.3.3 Cloud Pharmaceuticals, Inc Machine Learning in Pharmaceutical Products, Services and Solutions
7.3.4 Cloud Pharmaceuticals, Inc Machine Learning in Pharmaceutical Revenue (US$ Million) & (2019-2024)
7.3.5 Deep Genomics Recent Developments
7.4 Deep Genomics
7.4.1 Deep Genomics Profile
7.4.2 Deep Genomics Main Business
7.4.3 Deep Genomics Machine Learning in Pharmaceutical Products, Services and Solutions
7.4.4 Deep Genomics Machine Learning in Pharmaceutical Revenue (US$ Million) & (2019-2024)
7.4.5 Deep Genomics Recent Developments
7.5 Atomwise Inc.
7.5.1 Atomwise Inc. Profile
7.5.2 Atomwise Inc. Main Business
7.5.3 Atomwise Inc. Machine Learning in Pharmaceutical Products, Services and Solutions
7.5.4 Atomwise Inc. Machine Learning in Pharmaceutical Revenue (US$ Million) & (2019-2024)
7.5.5 Atomwise Inc. Recent Developments
7.6 Alphabet Inc.
7.6.1 Alphabet Inc. Profile
7.6.2 Alphabet Inc. Main Business
7.6.3 Alphabet Inc. Machine Learning in Pharmaceutical Products, Services and Solutions
7.6.4 Alphabet Inc. Machine Learning in Pharmaceutical Revenue (US$ Million) & (2019-2024)
7.6.5 Alphabet Inc. Recent Developments
7.7 NVIDIA Corporation
7.7.1 NVIDIA Corporation Profile
7.7.2 NVIDIA Corporation Main Business
7.7.3 NVIDIA Corporation Machine Learning in Pharmaceutical Products, Services and Solutions
7.7.4 NVIDIA Corporation Machine Learning in Pharmaceutical Revenue (US$ Million) & (2019-2024)
7.7.5 NVIDIA Corporation Recent Developments
7.8 International Business Machines Corporation
7.8.1 International Business Machines Corporation Profile
7.8.2 International Business Machines Corporation Main Business
7.8.3 International Business Machines Corporation Machine Learning in Pharmaceutical Products, Services and Solutions
7.8.4 International Business Machines Corporation Machine Learning in Pharmaceutical Revenue (US$ Million) & (2019-2024)
7.8.5 International Business Machines Corporation Recent Developments
7.9 Microsoft Corporation
7.9.1 Microsoft Corporation Profile
7.9.2 Microsoft Corporation Main Business
7.9.3 Microsoft Corporation Machine Learning in Pharmaceutical Products, Services and Solutions
7.9.4 Microsoft Corporation Machine Learning in Pharmaceutical Revenue (US$ Million) & (2019-2024)
7.9.5 Microsoft Corporation Recent Developments
7.10 IBM
7.10.1 IBM Profile
7.10.2 IBM Main Business
7.10.3 IBM Machine Learning in Pharmaceutical Products, Services and Solutions
7.10.4 IBM Machine Learning in Pharmaceutical Revenue (US$ Million) & (2019-2024)
7.10.5 IBM Recent Developments
8 Industry Chain Analysis
8.1 Machine Learning in Pharmaceutical Industrial Chain
8.2 Machine Learning in Pharmaceutical Upstream Analysis
8.2.1 Key Raw Materials
8.2.2 Raw Materials Key Suppliers
8.2.3 Manufacturing Cost Structure
8.3 Midstream Analysis
8.4 Downstream Analysis (Customers Analysis)
8.5 Sales Model and Sales Channels
8.5.1 Machine Learning in Pharmaceutical Sales Model
8.5.2 Sales Channel
8.5.3 Machine Learning in Pharmaceutical Distributors
9 Research Findings and Conclusion
10 Appendix
10.1 Research Methodology
10.1.1 Methodology/Research Approach
10.1.2 Data Source
10.2 Author Details
10.3 Disclaimer
List of Tables
Table 1. Machine Learning in Pharmaceutical Market Trends
Table 2. Machine Learning in Pharmaceutical Market Drivers & Opportunity
Table 3. Machine Learning in Pharmaceutical Market Challenges
Table 4. Machine Learning in Pharmaceutical Market Restraints
Table 5. Global Machine Learning in Pharmaceutical Revenue by Company (2019-2024) & (US$ Million)
Table 6. Global Machine Learning in Pharmaceutical Revenue Market Share by Company (2019-2024)
Table 7. Key Companies Machine Learning in Pharmaceutical Manufacturing Base Distribution and Headquarters
Table 8. Key Companies Machine Learning in Pharmaceutical Product Type
Table 9. Key Companies Time to Begin Mass Production of Machine Learning in Pharmaceutical
Table 10. Global Machine Learning in Pharmaceutical Companies Market Concentration Ratio (CR5 and HHI)
Table 11. Global Top Companies Market Share by Company Type (Tier 1, Tier 2, and Tier 3) & (based on the Revenue in Machine Learning in Pharmaceutical as of 2023)
Table 12. Mergers & Acquisitions, Expansion Plans
Table 13. Global Machine Learning in Pharmaceutical Sales Value by Type: 2019 VS 2023 VS 2030 (US$ Million)
Table 14. Global Machine Learning in Pharmaceutical Sales Value by Type (2019-2024) & (US$ Million)
Table 15. Global Machine Learning in Pharmaceutical Sales Value by Type (2025-2030) & (US$ Million)
Table 16. Global Machine Learning in Pharmaceutical Sales Market Share in Value by Type (2019-2024) & (%)
Table 17. Global Machine Learning in Pharmaceutical Sales Market Share in Value by Type (2025-2030) & (%)
Table 18. Global Machine Learning in Pharmaceutical Sales Value by Application: 2019 VS 2023 VS 2030 (US$ Million)
Table 19. Global Machine Learning in Pharmaceutical Sales Value by Application (2019-2024) & (US$ Million)
Table 20. Global Machine Learning in Pharmaceutical Sales Value by Application (2025-2030) & (US$ Million)
Table 21. Global Machine Learning in Pharmaceutical Sales Market Share in Value by Application (2019-2024) & (%)
Table 22. Global Machine Learning in Pharmaceutical Sales Market Share in Value by Application (2025-2030) & (%)
Table 23. Global Machine Learning in Pharmaceutical Sales Value by Region: 2019 VS 2023 VS 2030 (US$ Million)
Table 24. Global Machine Learning in Pharmaceutical Sales Value by Region (2019-2024) & (US$ Million)
Table 25. Global Machine Learning in Pharmaceutical Sales Value by Region (2025-2030) & (US$ Million)
Table 26. Global Machine Learning in Pharmaceutical Sales Value by Region (2019-2024) & (%)
Table 27. Global Machine Learning in Pharmaceutical Sales Value by Region (2025-2030) & (%)
Table 28. Key Countries/Regions Machine Learning in Pharmaceutical Sales Value Growth Trends, (US$ Million): 2019 VS 2023 VS 2030
Table 29. Key Countries/Regions Machine Learning in Pharmaceutical Sales Value, (2019-2024) & (US$ Million)
Table 30. Key Countries/Regions Machine Learning in Pharmaceutical Sales Value, (2025-2030) & (US$ Million)
Table 31. Cyclica inc Basic Information List
Table 32. Cyclica inc Description and Business Overview
Table 33. Cyclica inc Machine Learning in Pharmaceutical Products, Services and Solutions
Table 34. Revenue (US$ Million) in Machine Learning in Pharmaceutical Business of Cyclica inc (2019-2024)
Table 35. Cyclica inc Recent Developments
Table 36. BioSymetrics Inc. Basic Information List
Table 37. BioSymetrics Inc. Description and Business Overview
Table 38. BioSymetrics Inc. Machine Learning in Pharmaceutical Products, Services and Solutions
Table 39. Revenue (US$ Million) in Machine Learning in Pharmaceutical Business of BioSymetrics Inc. (2019-2024)
Table 40. BioSymetrics Inc. Recent Developments
Table 41. Cloud Pharmaceuticals, Inc Basic Information List
Table 42. Cloud Pharmaceuticals, Inc Description and Business Overview
Table 43. Cloud Pharmaceuticals, Inc Machine Learning in Pharmaceutical Products, Services and Solutions
Table 44. Revenue (US$ Million) in Machine Learning in Pharmaceutical Business of Cloud Pharmaceuticals, Inc (2019-2024)
Table 45. Cloud Pharmaceuticals, Inc Recent Developments
Table 46. Deep Genomics Basic Information List
Table 47. Deep Genomics Description and Business Overview
Table 48. Deep Genomics Machine Learning in Pharmaceutical Products, Services and Solutions
Table 49. Revenue (US$ Million) in Machine Learning in Pharmaceutical Business of Deep Genomics (2019-2024)
Table 50. Deep Genomics Recent Developments
Table 51. Atomwise Inc. Basic Information List
Table 52. Atomwise Inc. Description and Business Overview
Table 53. Atomwise Inc. Machine Learning in Pharmaceutical Products, Services and Solutions
Table 54. Revenue (US$ Million) in Machine Learning in Pharmaceutical Business of Atomwise Inc. (2019-2024)
Table 55. Atomwise Inc. Recent Developments
Table 56. Alphabet Inc. Basic Information List
Table 57. Alphabet Inc. Description and Business Overview
Table 58. Alphabet Inc. Machine Learning in Pharmaceutical Products, Services and Solutions
Table 59. Revenue (US$ Million) in Machine Learning in Pharmaceutical Business of Alphabet Inc. (2019-2024)
Table 60. Alphabet Inc. Recent Developments
Table 61. NVIDIA Corporation Basic Information List
Table 62. NVIDIA Corporation Description and Business Overview
Table 63. NVIDIA Corporation Machine Learning in Pharmaceutical Products, Services and Solutions
Table 64. Revenue (US$ Million) in Machine Learning in Pharmaceutical Business of NVIDIA Corporation (2019-2024)
Table 65. NVIDIA Corporation Recent Developments
Table 66. International Business Machines Corporation Basic Information List
Table 67. International Business Machines Corporation Description and Business Overview
Table 68. International Business Machines Corporation Machine Learning in Pharmaceutical Products, Services and Solutions
Table 69. Revenue (US$ Million) in Machine Learning in Pharmaceutical Business of International Business Machines Corporation (2019-2024)
Table 70. International Business Machines Corporation Recent Developments
Table 71. Microsoft Corporation Basic Information List
Table 72. Microsoft Corporation Description and Business Overview
Table 73. Microsoft Corporation Machine Learning in Pharmaceutical Products, Services and Solutions
Table 74. Revenue (US$ Million) in Machine Learning in Pharmaceutical Business of Microsoft Corporation (2019-2024)
Table 75. Microsoft Corporation Recent Developments
Table 76. IBM Basic Information List
Table 77. IBM Description and Business Overview
Table 78. IBM Machine Learning in Pharmaceutical Products, Services and Solutions
Table 79. Revenue (US$ Million) in Machine Learning in Pharmaceutical Business of IBM (2019-2024)
Table 80. IBM Recent Developments
Table 81. Key Raw Materials Lists
Table 82. Raw Materials Key Suppliers Lists
Table 83. Machine Learning in Pharmaceutical Downstream Customers
Table 84. Machine Learning in Pharmaceutical Distributors List
Table 85. Research Programs/Design for This Report
Table 86. Key Data Information from Secondary Sources
Table 87. Key Data Information from Primary Sources
Table 88. Business Unit and Senior & Team Lead Analysts
List of Figures
Figure 1. Machine Learning in Pharmaceutical Product Picture
Figure 2. Global Machine Learning in Pharmaceutical Sales Value, 2019 VS 2023 VS 2030 (US$ Million)
Figure 3. Global Machine Learning in Pharmaceutical Sales Value (2019-2030) & (US$ Million)
Figure 4. Machine Learning in Pharmaceutical Report Years Considered
Figure 5. Global Machine Learning in Pharmaceutical Players Revenue Ranking (2023) & (US$ Million)
Figure 6. The 5 and 10 Largest Manufacturers in the World: Market Share by Machine Learning in Pharmaceutical Revenue in 2023
Figure 7. Machine Learning in Pharmaceutical Market Share by Company Type (Tier 1, Tier 2, and Tier 3): 2019 VS 2023
Figure 8. Pharmaceutical Picture
Figure 9. Clinical Use Picture
Figure 10. Others Picture
Figure 11. Global Machine Learning in Pharmaceutical Sales Value by Type (2019 VS 2023 VS 2030) & (US$ Million)
Figure 12. Global Machine Learning in Pharmaceutical Sales Value Market Share by Type, 2023 & 2030
Figure 13. Product Picture of Drug Development
Figure 14. Product Picture of Clinical Trials
Figure 15. Product Picture of Patient Care
Figure 16. Product Picture of Others
Figure 17. Global Machine Learning in Pharmaceutical Sales Value by Application (2019 VS 2023 VS 2030) & (US$ Million)
Figure 18. Global Machine Learning in Pharmaceutical Sales Value Market Share by Application, 2023 & 2030
Figure 19. North America Machine Learning in Pharmaceutical Sales Value (2019-2030) & (US$ Million)
Figure 20. North America Machine Learning in Pharmaceutical Sales Value by Country (%), 2023 VS 2030
Figure 21. Europe Machine Learning in Pharmaceutical Sales Value (2019-2030) & (US$ Million)
Figure 22. Europe Machine Learning in Pharmaceutical Sales Value by Country (%), 2023 VS 2030
Figure 23. Asia Pacific Machine Learning in Pharmaceutical Sales Value (2019-2030) & (US$ Million)
Figure 24. Asia Pacific Machine Learning in Pharmaceutical Sales Value by Country (%), 2023 VS 2030
Figure 25. South America Machine Learning in Pharmaceutical Sales Value (2019-2030) & (US$ Million)
Figure 26. South America Machine Learning in Pharmaceutical Sales Value by Country (%), 2023 VS 2030
Figure 27. Middle East & Africa Machine Learning in Pharmaceutical Sales Value (2019-2030) & (US$ Million)
Figure 28. Middle East & Africa Machine Learning in Pharmaceutical Sales Value by Country (%), 2023 VS 2030
Figure 29. Key Countries/Regions Machine Learning in Pharmaceutical Sales Value (%), (2019-2030)
Figure 30. United States Machine Learning in Pharmaceutical Sales Value, (2019-2030) & (US$ Million)
Figure 31. United States Machine Learning in Pharmaceutical Sales Value by Type (%), 2023 VS 2030
Figure 32. United States Machine Learning in Pharmaceutical Sales Value by Application (%), 2023 VS 2030
Figure 33. Europe Machine Learning in Pharmaceutical Sales Value, (2019-2030) & (US$ Million)
Figure 34. Europe Machine Learning in Pharmaceutical Sales Value by Type (%), 2023 VS 2030
Figure 35. Europe Machine Learning in Pharmaceutical Sales Value by Application (%), 2023 VS 2030
Figure 36. China Machine Learning in Pharmaceutical Sales Value, (2019-2030) & (US$ Million)
Figure 37. China Machine Learning in Pharmaceutical Sales Value by Type (%), 2023 VS 2030
Figure 38. China Machine Learning in Pharmaceutical Sales Value by Application (%), 2023 VS 2030
Figure 39. Japan Machine Learning in Pharmaceutical Sales Value, (2019-2030) & (US$ Million)
Figure 40. Japan Machine Learning in Pharmaceutical Sales Value by Type (%), 2023 VS 2030
Figure 41. Japan Machine Learning in Pharmaceutical Sales Value by Application (%), 2023 VS 2030
Figure 42. South Korea Machine Learning in Pharmaceutical Sales Value, (2019-2030) & (US$ Million)
Figure 43. South Korea Machine Learning in Pharmaceutical Sales Value by Type (%), 2023 VS 2030
Figure 44. South Korea Machine Learning in Pharmaceutical Sales Value by Application (%), 2023 VS 2030
Figure 45. Southeast Asia Machine Learning in Pharmaceutical Sales Value, (2019-2030) & (US$ Million)
Figure 46. Southeast Asia Machine Learning in Pharmaceutical Sales Value by Type (%), 2023 VS 2030
Figure 47. Southeast Asia Machine Learning in Pharmaceutical Sales Value by Application (%), 2023 VS 2030
Figure 48. India Machine Learning in Pharmaceutical Sales Value, (2019-2030) & (US$ Million)
Figure 49. India Machine Learning in Pharmaceutical Sales Value by Type (%), 2023 VS 2030
Figure 50. India Machine Learning in Pharmaceutical Sales Value by Application (%), 2023 VS 2030
Figure 51. Machine Learning in Pharmaceutical Industrial Chain
Figure 52. Machine Learning in Pharmaceutical Manufacturing Cost Structure
Figure 53. Channels of Distribution (Direct Sales, and Distribution)
Figure 54. Bottom-up and Top-down Approaches for This Report
Figure 55. Data Triangulation
