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Generative AI in Personalized Medicine Market Size, Growth, Demands Outlook and Forecasts to 2032

The generative AI in personalized medicine market provides a deep study of market segments including type, end-use, and region. The report tracks the latest market trends and analyses their overall impact on the market. It also evaluates the market dynamics, which cover the key demand and price indicators, and studies the market on the basis of the SWOT and Porter's Five Forces models.

Generative AI in Personalized Medicine Market Size 2023 To 2032

Key Takeaways:

  • North America is expected to dominate in the generative AI in personalized medicine market during the forecast period.
  • By personalized medicine therapeutics, the pharmaceutical segment is expected to hold a leading position in the generative AI in personalized medicine market.
  • By deployment model, the cloud-based segment is expected to carry a significant share of the generative AI in personalized medicine market during the forecast period.
  • By end-user, the hospitals and clinics segment shares the maximum CAGR during the projection period.

The generative AI in personalized medicine report offers a comprehensive study of the current state expected at the major drivers, market strategies, and key vendors’ growth. The report presents energetic visions to conclude and study the market size, market hopes, and competitive surroundings. The research also focuses on the important achievements of the market, research & development, and regional growth of the leading competitors operating in the market. The current trends of the global generative AI in personalized medicine in conjunction with the geographical landscape of this vertical have also been included in this report.

The report offers intricate dynamics about different aspects of the global generative AI in personalized medicine market, which aids companies operating in the market in making strategic development decisions. The study also elaborates on significant changes that are highly anticipated to configure growth of the global generative AI in personalized medicine during the forecast period. It also includes a key indicator assessment that highlights growth prospects of this market and estimates statistics related to growth of the market in terms of value (US$ Mn) and volume (tons).

Sample Link @ https://www.precedenceresearch.com/sample/3095

This study covers a detailed segmentation of the global generative AI in personalized medicine market, along with key information and a competition outlook. The report mentions company profiles of players that are currently dominating the global generative AI in personalized medicine market, wherein various developments, expansions, and winning strategies practiced and implemented by leading players have been presented in detail.

Key Players

  • Butterfly Network
  • Deep Genomics
  • Google LLC
  • IBM Watson Health
  • Microsoft Corporation
  • Aidoc
  • Insilico Medicine
  • PathAI
  • Tencent Holdings Ltd.
  • Neuralink Corporation
  • Johnson & Johnson

Market Segmentation

By Personalized Medicine Therapeutics

  • Pharmaceutical
  • Genomic Medicine
  • Devices

By Deployment Model

  • On-premise
  • Cloud Based

By End-User

  • Hospitals and Clinics
  • Clinical Research
  • Healthcare Organizations
  • Diagnostic Centers

By Geography

  • North America
  • Europe
  • Asia-Pacific
  • Latin America
  • Middle East and Africa

Research Methodology

The research methodology adopted by analysts for compiling the global generative AI in personalized medicine report is based on detailed primary as well as secondary research. With the help of in-depth insights of the market-affiliated information that is obtained and legitimated by market-admissible resources, analysts have offered riveting observations and authentic forecasts for the global market.

During the primary research phase, analysts interviewed market stakeholders, investors, brand managers, vice presidents, and sales and marketing managers. Based on data obtained through interviews of genuine resources, analysts have emphasized the changing scenario of the global market.

For secondary research, analysts scrutinized numerous annual report publications, white papers, market association publications, and company websites to obtain the necessary understanding of the global generative AI in personalized medicine market.

TABLE OF CONTENT

Chapter 1. Introduction

1.1. Research Objective

1.2. Scope of the Study

1.3. Definition

Chapter 2. Research Methodology (Premium Insights)

2.1. Research Approach

2.2. Data Sources

2.3. Assumptions & Limitations

Chapter 3. Executive Summary

3.1. Market Snapshot

Chapter 4. Market Variables and Scope 

4.1. Introduction

4.2. Market Classification and Scope

4.3. Industry Value Chain Analysis

4.3.1. Raw Material Procurement Analysis 

4.3.2. Sales and Distribution Channel Analysis

4.3.3. Downstream Buyer Analysis

Chapter 5. COVID 19 Impact on Generative AI in Personalized Medicine Market 

5.1. COVID-19 Landscape: Generative AI in Personalized Medicine Industry Impact

5.2. COVID 19 - Impact Assessment for the Industry

5.3. COVID 19 Impact: Global Major Government Policy

5.4. Market Trends and Opportunities in the COVID-19 Landscape

Chapter 6. Market Dynamics Analysis and Trends

6.1. Market Dynamics

6.1.1. Market Drivers

6.1.2. Market Restraints

6.1.3. Market Opportunities

6.2. Porter’s Five Forces Analysis

6.2.1. Bargaining power of suppliers

6.2.2. Bargaining power of buyers

6.2.3. Threat of substitute

6.2.4. Threat of new entrants

6.2.5. Degree of competition

Chapter 7. Competitive Landscape

7.1.1. Company Market Share/Positioning Analysis

7.1.2. Key Strategies Adopted by Players

7.1.3. Vendor Landscape

7.1.3.1. List of Suppliers

7.1.3.2. List of Buyers

Chapter 8. Global Generative AI in Personalized Medicine Market, By Personalized Medicine Therapeutics

8.1. Generative AI in Personalized Medicine Market, by Personalized Medicine Therapeutics, 2023-2032

8.1.1 Pharmaceutical

8.1.1.1. Market Revenue and Forecast (2020-2032)

8.1.2. Genomic Medicine

8.1.2.1. Market Revenue and Forecast (2020-2032)

8.1.3. Devices

8.1.3.1. Market Revenue and Forecast (2020-2032)

Chapter 9. Global Generative AI in Personalized Medicine Market, By Deployment Model

9.1. Generative AI in Personalized Medicine Market, by Deployment Model, 2023-2032

9.1.1. On-premise

9.1.1.1. Market Revenue and Forecast (2020-2032)

9.1.2. Cloud Based

9.1.2.1. Market Revenue and Forecast (2020-2032)

Chapter 10. Global Generative AI in Personalized Medicine Market, By End-User 

10.1. Generative AI in Personalized Medicine Market, by End-User, 2023-2032

10.1.1. Hospitals and Clinics

10.1.1.1. Market Revenue and Forecast (2020-2032)

10.1.2. Clinical Research

10.1.2.1. Market Revenue and Forecast (2020-2032)

10.1.3. Healthcare Organizations

10.1.3.1. Market Revenue and Forecast (2020-2032)

10.1.4. Diagnostic Centers

10.1.4.1. Market Revenue and Forecast (2020-2032)

Chapter 11. Global Generative AI in Personalized Medicine Market, Regional Estimates and Trend Forecast

11.1. North America

11.1.1. Market Revenue and Forecast, by Personalized Medicine Therapeutics (2020-2032)

11.1.2. Market Revenue and Forecast, by Deployment Model (2020-2032)

11.1.3. Market Revenue and Forecast, by End-User (2020-2032)

11.1.4. U.S.

11.1.4.1. Market Revenue and Forecast, by Personalized Medicine Therapeutics (2020-2032)

11.1.4.2. Market Revenue and Forecast, by Deployment Model (2020-2032)

11.1.4.3. Market Revenue and Forecast, by End-User (2020-2032)

11.1.5. Rest of North America

11.1.5.1. Market Revenue and Forecast, by Personalized Medicine Therapeutics (2020-2032)

11.1.5.2. Market Revenue and Forecast, by Deployment Model (2020-2032)

11.1.5.3. Market Revenue and Forecast, by End-User (2020-2032)

11.2. Europe

11.2.1. Market Revenue and Forecast, by Personalized Medicine Therapeutics (2020-2032)

11.2.2. Market Revenue and Forecast, by Deployment Model (2020-2032)

11.2.3. Market Revenue and Forecast, by End-User (2020-2032)

11.2.4. UK

11.2.4.1. Market Revenue and Forecast, by Personalized Medicine Therapeutics (2020-2032)

11.2.4.2. Market Revenue and Forecast, by Deployment Model (2020-2032)

11.2.4.3. Market Revenue and Forecast, by End-User (2020-2032)

11.2.5. Germany

11.2.5.1. Market Revenue and Forecast, by Personalized Medicine Therapeutics (2020-2032)

11.2.5.2. Market Revenue and Forecast, by Deployment Model (2020-2032)

11.2.5.3. Market Revenue and Forecast, by End-User (2020-2032)

11.2.6. France

11.2.6.1. Market Revenue and Forecast, by Personalized Medicine Therapeutics (2020-2032)

11.2.6.2. Market Revenue and Forecast, by Deployment Model (2020-2032)

11.2.6.3. Market Revenue and Forecast, by End-User (2020-2032)

11.2.7. Rest of Europe

11.2.7.1. Market Revenue and Forecast, by Personalized Medicine Therapeutics (2020-2032)

11.2.7.2. Market Revenue and Forecast, by Deployment Model (2020-2032)

11.2.7.3. Market Revenue and Forecast, by End-User (2020-2032)

11.3. APAC

11.3.1. Market Revenue and Forecast, by Personalized Medicine Therapeutics (2020-2032)

11.3.2. Market Revenue and Forecast, by Deployment Model (2020-2032)

11.3.3. Market Revenue and Forecast, by End-User (2020-2032)

11.3.4. India

11.3.4.1. Market Revenue and Forecast, by Personalized Medicine Therapeutics (2020-2032)

11.3.4.2. Market Revenue and Forecast, by Deployment Model (2020-2032)

11.3.4.3. Market Revenue and Forecast, by End-User (2020-2032)

11.3.5. China

11.3.5.1. Market Revenue and Forecast, by Personalized Medicine Therapeutics (2020-2032)

11.3.5.2. Market Revenue and Forecast, by Deployment Model (2020-2032)

11.3.5.3. Market Revenue and Forecast, by End-User (2020-2032)

11.3.6. Japan

11.3.6.1. Market Revenue and Forecast, by Personalized Medicine Therapeutics (2020-2032)

11.3.6.2. Market Revenue and Forecast, by Deployment Model (2020-2032)

11.3.6.3. Market Revenue and Forecast, by End-User (2020-2032)

11.3.7. Rest of APAC

11.3.7.1. Market Revenue and Forecast, by Personalized Medicine Therapeutics (2020-2032)

11.3.7.2. Market Revenue and Forecast, by Deployment Model (2020-2032)

11.3.7.3. Market Revenue and Forecast, by End-User (2020-2032)

11.4. MEA

11.4.1. Market Revenue and Forecast, by Personalized Medicine Therapeutics (2020-2032)

11.4.2. Market Revenue and Forecast, by Deployment Model (2020-2032)

11.4.3. Market Revenue and Forecast, by End-User (2020-2032)

11.4.4. GCC

11.4.4.1. Market Revenue and Forecast, by Personalized Medicine Therapeutics (2020-2032)

11.4.4.2. Market Revenue and Forecast, by Deployment Model (2020-2032)

11.4.4.3. Market Revenue and Forecast, by End-User (2020-2032)

11.4.5. North Africa

11.4.5.1. Market Revenue and Forecast, by Personalized Medicine Therapeutics (2020-2032)

11.4.5.2. Market Revenue and Forecast, by Deployment Model (2020-2032)

11.4.5.3. Market Revenue and Forecast, by End-User (2020-2032)

11.4.6. South Africa

11.4.6.1. Market Revenue and Forecast, by Personalized Medicine Therapeutics (2020-2032)

11.4.6.2. Market Revenue and Forecast, by Deployment Model (2020-2032)

11.4.6.3. Market Revenue and Forecast, by End-User (2020-2032)

11.4.7. Rest of MEA

11.4.7.1. Market Revenue and Forecast, by Personalized Medicine Therapeutics (2020-2032)

11.4.7.2. Market Revenue and Forecast, by Deployment Model (2020-2032)

11.4.7.3. Market Revenue and Forecast, by End-User (2020-2032)

11.5. Latin America

11.5.1. Market Revenue and Forecast, by Personalized Medicine Therapeutics (2020-2032)

11.5.2. Market Revenue and Forecast, by Deployment Model (2020-2032)

11.5.3. Market Revenue and Forecast, by End-User (2020-2032)

11.5.4. Brazil

11.5.4.1. Market Revenue and Forecast, by Personalized Medicine Therapeutics (2020-2032)

11.5.4.2. Market Revenue and Forecast, by Deployment Model (2020-2032)

11.5.4.3. Market Revenue and Forecast, by End-User (2020-2032)

11.5.5. Rest of LATAM

11.5.5.1. Market Revenue and Forecast, by Personalized Medicine Therapeutics (2020-2032)

11.5.5.2. Market Revenue and Forecast, by Deployment Model (2020-2032)

11.5.5.3. Market Revenue and Forecast, by End-User (2020-2032)

Chapter 12. Company Profiles

12.1. Butterfly Network

12.1.1. Company Overview

12.1.2. Product Offerings

12.1.3. Financial Performance

12.1.4. Recent Initiatives

12.2. Deep Genomics

12.2.1. Company Overview

12.2.2. Product Offerings

12.2.3. Financial Performance

12.2.4. Recent Initiatives

12.3. Google LLC

12.3.1. Company Overview

12.3.2. Product Offerings

12.3.3. Financial Performance

12.3.4. Recent Initiatives

12.4. IBM Watson Health

12.4.1. Company Overview

12.4.2. Product Offerings

12.4.3. Financial Performance

12.4.4. Recent Initiatives

12.5. Microsoft Corporation

12.5.1. Company Overview

12.5.2. Product Offerings

12.5.3. Financial Performance

12.5.4. Recent Initiatives

12.6. Aidoc

12.6.1. Company Overview

12.6.2. Product Offerings

12.6.3. Financial Performance

12.6.4. Recent Initiatives

12.7. Insilico Medicine

12.7.1. Company Overview

12.7.2. Product Offerings

12.7.3. Financial Performance

12.7.4. Recent Initiatives

12.8. PathAI

12.8.1. Company Overview

12.8.2. Product Offerings

12.8.3. Financial Performance

12.8.4. Recent Initiatives

12.9. Tencent Holdings Ltd.

12.9.1. Company Overview

12.9.2. Product Offerings

12.9.3. Financial Performance

12.9.4. Recent Initiatives

12.10. Neuralink Corporation

12.10.1. Company Overview

12.10.2. Product Offerings

12.10.3. Financial Performance

12.10.4. Recent Initiatives

Chapter 13. Research Methodology

13.1. Primary Research

13.2. Secondary Research

13.3. Assumptions

Chapter 14. Appendix

14.1. About Us

14.2. Glossary of Terms

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