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市場調查報告書
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1958434

全球生命科學人工智慧市場（至 2040 年）：依部署方式、交付類型、技術類型、應用領域和主要地區劃分：行業趨勢和預測

Artificial Intelligence in Life Sciences Market, till 2040: Distribution by Deployment Mode, Type of Offering, Type of Technology, Application Areas and Key Geographical Regions: Industry Trends and Global Forecasts
出版日期: | 出版商: Roots Analysis | 英文 179 Pages | 商品交期: 7-10個工作天內

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簡介目錄

生命科學人工智慧市場展望

預計到 2040 年，全球生命科學人工智慧市場規模將達到 730.5 億美元，較目前的 56.9 億美元年複合成長率 (CAGR) 為 20%。

人工智慧正在革新生命科學領域，包括生物學、製藥、生物技術和醫學等領域。這些領域致力於透過研究生物系統和治療創新來改善人類健康。人工智慧作為一種先進的運算框架，利用機器學習演算法處理大量資料集、識別複雜模式，並以前所未有的效率產生預測性見解。

由於基因組數據、患者數據和臨床試驗數據的指數級增長，生命科學人工智慧市場正經歷強勁增長。 如此龐大的資料量需要快速且有效率的分析，而人工智慧 (AI) 的優勢在於能夠比傳統方法更準確地處理大型資料集。此外，AI 還能加速藥物研發進程，大幅降低不斷上漲的研發成本，並提高臨床試驗的效率。機器學習和雲端運算技術的進步，以及製藥公司的大量投資，也正在推動市場的發展動能。

生命科學領域的人工智慧市場-IMG1

推動生命科學領域人工智慧市場成長的關鍵驅動因素

推動生命科學領域人工智慧市場快速擴張的驅動因素有很多。基因組分析、患者記錄和臨床試驗產生的數據量呈指數級增長，需要快速而精確的分析。人工智慧在速度和準確性方面超越了傳統的人工方法，透過有效預測分子相互作用來加速藥物發現。它還能透過更精準的患者篩選和結果預測來優化臨床試驗並最大限度地降低失敗率。

對精準醫療日益增長的需求進一步加速了人工智慧的應用，因為人工智慧可以根據個體的基因和健康狀況量身定制治療方案，從而提高療效。機器學習演算法和雲端運算的進步使得跨研究環境的無縫整合成為可能。此外，大型製藥公司正在與Google和IBM等技術領導者建立策略合作夥伴關係，並投入大量資金。這些因素正在推動生命科學領域人工智慧市場在預測期內的整體成長。

生命科學領域人工智慧市場：競爭格局

生命科學領域人工智慧的競爭格局由大型科技公司、製藥業領導者和專業新創公司組成，正在推動藥物發現、臨床試驗和個人化醫療領域的創新。 IBM、IQVIA和Oracle等公司提供全端平台，全面解決資料整合、人工智慧模型訓練和法規遵循問題。 羅氏、輝瑞和Insilico Medicine等製藥公司正在利用人工智慧分析海量基因組和臨床數據，加速藥物研發，從而降低成本並加快產品上市速度。 Atomwise、Sophia Genetics和NuMedii等新興公司則專注於分子模擬、基因組分析和預測建模等細分領域工具。

生命科學領域人工智慧的演進：新興產業趨勢

生命科學領域人工智慧的關鍵趨勢包括加速藥物研發、個人化醫療以及利用影像和穿戴式技術進行先進診斷。其他發展方向包括透過人工智慧科學工廠實現實驗室工作流程自動化、用於監管申報的大規模領域特定語言模型、增強臨床試驗優化以及將人工智慧無縫整合到藥物警戒和預防保健策略中。 這些進步利用大量的生物和臨床資料集，優先考慮營運效率、成本降低和準確性，從而實現以患者為中心的醫療服務。

本報告分析了全球生命科學人工智慧市場，並提供了市場規模估算、機會分析、競爭格局和公司概況。

目錄

第一部分：報告概述

第一章：引言

第二章：研究方法

第三章：市場動態

第四章：宏觀經濟指標

第二部分：質性研究結果

第五章：摘要整理

第六章：引言

第七章：監理環境

第三部分：市場概覽

第八章：關鍵指標綜合資料庫

公司

第 9 章：競爭格局

第 10 章：競爭分析

第 11 章：生命科學人工智慧新創企業生態系統

第 4 部分：公司簡介

第12章 企業簡介

  • 章概要
  • Atomwise
  • BenevolentAI
  • Exscientia
  • Foundation Medicine
  • GE HealthCare
  • IBM
  • Insilico Medicine
  • Microsoft
  • NVIDIA
  • Owkin
  • PathAI
  • Recursion
  • Schrodinger
  • Tempus AI

第 5 節市場趨勢

第 13 章：大趨勢分析

第 14 章：專利分析

第 15 章：最新進展

第 6 部分：市場機會分析

第 16 章：全球生命科學人工智慧市場

第 17 章：依部署方法劃分的市場機會

第 18 章：市場依產品類型劃分的市場機會

第19章：依技術類型劃分的市場機會

第20章：依應用領域劃分的市場機會

第21章：北美生命科學人工智慧市場機會

第22章：歐洲生命科學人工智慧市場機會

第23章：亞洲生命科學人工智慧市場機會

第24章：中東與北非（MENA）生命科學人工智慧市場機會

第25章：拉丁美洲生命科學人工智慧市場機會

第26章：世界其他地區生命科學人工智慧市場機會

市場

第27章：主要參與者的市場集中度分析

第28章：鄰近市場分析

第7節：策略工具

第29章：關鍵成功策略

第30章：波特五力分析

第31章：SWOT分析

第32章：Roots策略建議

第8節：其他獨家發現

第33章：主要研究發現

第34章：報告結論

第9節：附錄

簡介目錄
Product Code: RAD00033

Artificial Intelligence In Life Sciences Market Outlook

As per Roots Analysis, the global Artificial intelligence in life sciences market size is estimated to grow from USD 5.69 billion in current year to USD 73.05 billion by 2040, at a CAGR of 20% during the forecast period, till 2040.

Artificial intelligence (AI) is revolutionizing the life sciences sector, encompassing disciplines such as biology, pharmaceuticals, biotechnology, and medicine. These fields focus on advancing human health through the study of biological systems and therapeutic innovations. AI functions as an advanced computational framework, leveraging machine learning algorithms to process vast datasets, identify intricate patterns, and generate predictive insights with unprecedented efficiency.

The artificial intelligence in life sciences market is experiencing robust growth, fueled by the exponential increase in genomic, patient, and clinical trial data. Such huge data necessitates rapid, efficient analysis, where AI outperforms traditional methods by processing vast datasets with precision. Further, AI accelerates drug discovery timelines and significantly reduces elevated R&D expenditures and enhances clinical trial efficiency. Additionally, technological advancements in machine learning, cloud computing, and substantial investments by pharmaceutical giants also fuel the momentum of the market.

Artificial Intelligence in Life Sciences Market - IMG1

Strategic Insights for Senior Leaders

Transformative Role of Artificial Intelligence in Drug Discovery and Personalized Medicine

Artificial intelligence (AI) is revolutionizing drug discovery by speeding up the process, lowering costs, and enhancing success rates through methods, such as virtual screening, predictive modeling for efficacy and toxicity, and de novo drug design. Machine learning and deep learning techniques evaluate large datasets to pinpoint promising drug candidates, anticipate their behavior within the body, and even create completely new molecules. AI is also applied in drug repurposing and personalizing therapies by discovering new applications for existing medications or customizing treatments for individual patients based on their specific data.

In personalized medicine, AI integrates individual genomic profiles, lifestyle factors, and historical health data to develop tailored treatment strategies, forecast therapeutic responses, and dynamically adjust dosages or regimens. This minimized adverse effects, improving efficacy, and promoting patient adherence through automated reminders. Collectively, these capabilities reduce healthcare costs, expand access, and facilitates home-based models.

Key Drivers Propelling Growth of Artificial Intelligence (AI) in Life Sciences Market

Several key drivers propel the rapid expansion of AI in life sciences market. Exponential growth in data volumes from genomics, patient records, and clinical trials demands swift, precise analysis. AI surpasses traditional manual approaches in speed and accuracy and accelerates drug discovery by predicting molecular interactions effectively. It also optimizes clinical trials through superior patient selection and outcome forecasting, minimizing failure rates.

Rising demand for precision medicine further accelerates adoption, as AI tailors therapies to individual genetic and health profiles for enhanced efficacy. Advancements in machine learning algorithms and cloud computing facilitate seamless integration across research environments. Further, pharmaceutical giants are forging strategic partnerships with tech leaders like Google and IBM, backed by substantial investments. Collectively, these factors are propelling the growth of the overall AI in life sciences market during the forecast period.

Artificial Intelligence in Life Sciences Market: Competitive Landscape of Companies in this Industry

The competitive landscape of AI in life sciences features a mix of big tech giants, pharma leaders, and specialized startups driving innovation in drug discovery, clinical trials, and personalized medicine. Companies like IBM, IQVIA, and Oracle offer full-stack platforms that handle data integration, AI model training, and regulatory compliance. Pharma players such as Roche, Pfizer, and Insilico Medicine use AI to speed up drug development by analyzing vast genomic and clinical datasets, cutting costs and time-to-market. Emerging challengers like Atomwise, Sophia Genetics, and NuMedii focus on niche tools for molecular simulations, genomic analysis, and predictive modeling.

Artificial Intelligence in Life Sciences Evolution: Emerging Trends in the Industry

Key trends in the AI life sciences sector include accelerated AI-driven drug discovery, personalized medicine, and advanced diagnostics leveraging imaging and wearables. Additional developments encompass automation of laboratory workflows through AI-Science Factories, domain-specific large language models for regulatory applications, enhanced clinical trial optimization, and seamless AI integration for pharmacovigilance and preventative health strategies. These developments prioritize operational efficiency, cost reduction, and precision by harnessing vast biological and clinical datasets to enable proactive, patient-centric healthcare delivery.

Key Market Challenges

The Artificial intelligence in life sciences market faces several key challenges that hinder widespread adoption. High development costs for AI algorithms, genomic sequencing, and personalized therapies strain budgets, especially for smaller biotech firms. Data privacy concerns and regulatory hurdles, including stringent FDA guidelines on AI validation and ethical AI use, slow down approvals and integration into clinical workflows.

Additionally, problems like AI bias, stemming from training data that underrepresents certain patient groups can lead to unfair treatment results for diverse populations. This erodes trust among healthcare providers. Limited interoperability between disparate healthcare systems and AI platforms further complicates real-world data sharing for biomarker discovery and treatment customization. Despite these obstacles, ongoing innovations in federated learning offer pathways to overcome them, supporting sustained market growth.

Regional Analysis: North America to Hold the Largest Share in the Market

According to our estimates North America currently captures a significant share of Artificial Intelligence in life sciences market. This can be attributed to surging chronic disease burden, including cancer, diabetes, and infectious conditions. Robust R&D investments in AI-driven solutions, combined with advanced healthcare infrastructure and rapid regulatory approvals, further accelerates adoption and innovation in personalized diagnostics and therapies.

Artificial Intelligence In Life Sciences Market: Key Market Segmentation

Deployment Mode

  • Cloud
  • On Premise

Type of Offering

  • Software
  • Hardware
  • Services

Type of Technology

  • Machine Learning
  • Computer Vision
  • Natural Language Processing
  • Predictive Analytics

Application Area

  • Medical Diagnosis
  • Drug Discovery
  • Precision & Personalized Medicine
  • Biotechnology
  • Clinical Trials
  • Patent Monitoring

Geographical Regions

  • North America
  • US
  • Canada
  • Mexico
  • Other North American countries
  • Europe
  • Austria
  • Belgium
  • Denmark
  • France
  • Germany
  • Ireland
  • Italy
  • Netherlands
  • Norway
  • Russia
  • Spain
  • Sweden
  • Switzerland
  • UK
  • Other European countries
  • Asia
  • China
  • India
  • Japan
  • Singapore
  • South Korea
  • Other Asian countries
  • Latin America
  • Brazil
  • Chile
  • Colombia
  • Venezuela
  • Other Latin American countries
  • Middle East and North Africa
  • Egypt
  • Iran
  • Iraq
  • Israel
  • Kuwait
  • Saudi Arabia
  • UAE
  • Other MENA countries
  • Rest of the World

Example Players in Artificial Intelligence in Life Sciences Market

  • Atomwise
  • BenevolentAI
  • Exscientia
  • Foundation Medicine
  • GE HealthCare
  • IBM
  • Insilico Medicine
  • Microsoft
  • NVIDIA
  • Owkin
  • PathAI
  • Recursion
  • Schrodinger
  • Tempus AI

Artificial Intelligence In Life Sciences Market: Report Coverage

The report on the Artificial intelligence in life sciences market features insights on various sections, including:

  • Market Sizing and Opportunity Analysis: An in-depth analysis of the artificial intelligence in life sciences market, focusing on key market segments, including [A] deployment mode, [B] type of offering, [C] type of technology, [D] application areas and [E] key geographical regions.
  • Competitive Landscape: A comprehensive analysis of the companies engaged in the artificial intelligence in life sciences market, based on several relevant parameters, such as [A] year of establishment, [B] company size, [C] location of headquarters and [D] ownership structure.
  • Company Profiles: Elaborate profiles of prominent players engaged in the artificial intelligence in life sciences market, providing details on [A] location of headquarters, [B] company size, [C] company mission, [D] company footprint, [E] management team, [F] contact details, [G] financial information, [H] operating business segments, [I] product / technology portfolio, [J] recent developments, and an informed future outlook.
  • Megatrends: An evaluation of ongoing megatrends in the artificial intelligence in life sciences industry.
  • Recent Developments: An overview of the recent developments made in the artificial intelligence in life sciences market, along with analysis based on relevant parameters, including [A] year of initiative, [B] type of initiative, [C] geographical distribution and [D] most active players.
  • SWOT Analysis: An insightful SWOT framework, highlighting the strengths, weaknesses, opportunities and threats in the domain. Additionally, it provides Harvey ball analysis, highlighting the relative impact of each SWOT parameter.

Key Questions Answered in this Report

  • What is the current and future market size?
  • Who are the leading companies in this market?
  • What are the growth drivers that are likely to influence the evolution of this market?
  • What are the key partnership and funding trends shaping this industry?
  • Which region is likely to grow at higher CAGR till 2040?
  • How is the current and future market opportunity likely to be distributed across key market segments?

Reasons to Buy this Report

  • Detailed Market Analysis: The report provides a comprehensive market analysis, offering detailed revenue projections of the overall market and its specific sub-segments. This information is valuable to both established market leaders and emerging entrants.
  • In-depth Analysis of Trends: Stakeholders can leverage the report to gain a deeper understanding of the competitive dynamics within the market. Each report maps ecosystem activity across partnerships, funding, and patent landscapes to reveal growth hotspots and white spaces in the industry.
  • Opinion of Industry Experts: The report features extensive interviews and surveys with key opinion leaders and industry experts to validate market trends mentioned in the report.
  • Decision-ready Deliverables: The report offers stakeholders with strategic frameworks (Porter's Five Forces, value chain, SWOT), and complimentary Excel / slide packs with customization support.

Additional Benefits

  • Complimentary Dynamic Excel Dashboards for Analytical Modules
  • Exclusive 15% Free Content Customization
  • Personalized Interactive Report Walkthrough with Our Expert Research Team
  • Free Report Updates for Versions Older than 6-12 Months

TABLE OF CONTENTS

SECTION I: REPORT OVERVIEW

1. PREFACE

  • 1.1. Introduction
  • 1.2. Market Share Insights
  • 1.3. Key Market Insights
  • 1.4. Report Coverage
  • 1.5. Key Questions Answered
  • 1.6. Chapter Outlines

2. RESEARCH METHODOLOGY

  • 2.1. Chapter Overview
  • 2.2. Research Assumptions
  • 2.3. Database Building
    • 2.3.1. Data Collection
    • 2.3.2. Data Validation
    • 2.3.3. Data Analysis
  • 2.4. Project Methodology
    • 2.4.1. Secondary Research
      • 2.4.1.1. Annual Reports
      • 2.4.1.2. Academic Research Papers
      • 2.4.1.3. Company Websites
      • 2.4.1.4. Investor Presentations
      • 2.4.1.5. Regulatory Filings
      • 2.4.1.6. White Papers
      • 2.4.1.7. Industry Publications
      • 2.4.1.8. Conferences and Seminars
      • 2.4.1.9. Government Portals
      • 2.4.1.10. Media and Press Releases
      • 2.4.1.11. Newsletters
      • 2.4.1.12. Industry Databases
      • 2.4.1.13. Roots Proprietary Databases
      • 2.4.1.14. Paid Databases and Sources
      • 2.4.1.15. Social Media Portals
      • 2.4.1.16. Other Secondary Sources
    • 2.4.2. Primary Research
      • 2.4.2.1. Introduction
      • 2.4.2.2. Types
        • 2.4.2.2.1. Qualitative
        • 2.4.2.2.2. Quantitative
      • 2.4.2.3. Advantages
      • 2.4.2.4. Techniques
        • 2.4.2.4.1. Interviews
        • 2.4.2.4.2. Surveys
        • 2.4.2.4.3. Focus Groups
        • 2.4.2.4.4. Observational Research
        • 2.4.2.4.5. Social Media Interactions
      • 2.4.2.5. Stakeholders
        • 2.4.2.5.1. Company Executives (CXOs)
        • 2.4.2.5.2. Board of Directors
        • 2.4.2.5.3. Company Presidents and Vice Presidents
        • 2.4.2.5.4. Key Opinion Leaders
        • 2.4.2.5.5. Research and Development Heads
        • 2.4.2.5.6. Technical Experts
        • 2.4.2.5.7. Subject Matter Experts
        • 2.4.2.5.8. Scientists
        • 2.4.2.5.9. Doctors and Other Healthcare Providers
      • 2.4.2.6. Ethics and Integrity
        • 2.4.2.6.1. Research Ethics
        • 2.4.2.6.2. Data Integrity
    • 2.4.3. Analytical Tools and Databases

3. MARKET DYNAMICS

  • 3.1. Forecast Methodology
    • 3.1.1. Top-Down Approach
    • 3.1.2. Bottom-Up Approach
    • 3.1.3. Hybrid Approach
  • 3.2. Market Assessment Framework
    • 3.2.1. Total Addressable Market (TAM)
    • 3.2.2. Serviceable Addressable Market (SAM)
    • 3.2.3. Serviceable Obtainable Market (SOM)
    • 3.2.4. Currently Acquired Market (CAM)
  • 3.3. Forecasting Tools and Techniques
    • 3.3.1. Qualitative Forecasting
    • 3.3.2. Correlation
    • 3.3.3. Regression
    • 3.3.4. Time Series Analysis
    • 3.3.5. Extrapolation
    • 3.3.6. Convergence
    • 3.3.7. Forecast Error Analysis
    • 3.3.8. Data Visualization
    • 3.3.9. Scenario Planning
    • 3.3.10. Sensitivity Analysis
  • 3.4. Key Considerations
    • 3.4.1. Demographics
    • 3.4.2. Market Access
    • 3.4.3. Reimbursement Scenarios
    • 3.4.4. Industry Consolidation
  • 3.5. Robust Quality Control
  • 3.6. Key Market Segmentations
  • 3.7. Limitations

4. MACRO-ECONOMIC INDICATORS

  • 4.1. Chapter Overview
  • 4.2. Market Dynamics
    • 4.2.1. Time Period
      • 4.2.1.1. Historical Trends
      • 4.2.1.2. Current and Forecasted Estimates
    • 4.2.2. Currency Coverage
      • 4.2.2.1. Overview of Major Currencies Affecting the Market
      • 4.2.2.2. Impact of Currency Fluctuations on the Industry
    • 4.2.3. Foreign Exchange Impact
      • 4.2.3.1. Evaluation of Foreign Exchange Rates and Their Impact on Market
      • 4.2.3.2. Strategies for Mitigating Foreign Exchange Risk
    • 4.2.4. Recession
      • 4.2.4.1. Historical Analysis of Past Recessions and Lessons Learnt
      • 4.2.4.2. Assessment of Current Economic Conditions and Potential Impact on the Market
    • 4.2.5. Inflation
      • 4.2.5.1. Measurement and Analysis of Inflationary Pressures in the Economy
      • 4.2.5.2. Potential Impact of Inflation on the Market Evolution
    • 4.2.6. Interest Rates
      • 4.2.6.1. Overview of Interest Rates and Their Impact on the Market
      • 4.2.6.2. Strategies for Managing Interest Rate Risk
    • 4.2.7. Commodity Flow Analysis
      • 4.2.7.1. Type of Commodity
      • 4.2.7.2. Origins and Destinations
      • 4.2.7.3. Values and Weights
      • 4.2.7.4. Modes of Transportation
    • 4.2.8. Global Trade Dynamics
      • 4.2.8.1. Import Scenario
      • 4.2.8.2. Export Scenario
    • 4.2.9. War Impact Analysis
      • 4.2.9.1. Russian-Ukraine War
      • 4.2.9.2. Israel-Hamas War
    • 4.2.10. COVID Impact / Related Factors
      • 4.2.10.1. Global Economic Impact
      • 4.2.10.2. Industry-specific Impact
      • 4.2.10.3. Government Response and Stimulus Measures
      • 4.2.10.4. Future Outlook and Adaptation Strategies
    • 4.2.11. Other Indicators
      • 4.2.11.1. Fiscal Policy
      • 4.2.11.2. Consumer Spending
      • 4.2.11.3. Gross Domestic Product (GDP)
      • 4.2.11.4. Employment
      • 4.2.11.5. Taxes
      • 4.2.11.6. R&D Innovation
      • 4.2.11.7. Stock Market Performance
      • 4.2.11.8. Supply Chain
      • 4.2.11.9. Cross-Border Dynamics

SECTION II: QUALITATIVE INSIGHTS

5. EXECUTIVE SUMMARY

6. INTRODUCTION

  • 6.1. Chapter Overview
  • 6.2. Overview of Artificial Intelligence in Life Sciences Market
    • 6.2.1. Historical Evolution
    • 6.2.2. Key Applications
    • 6.2.3. Impact on Healthcare
  • 6.3. Future Perspective

7. REGULATORY SCENARIO

SECTION III: MARKET OVERVIEW

8. COMPREHENSIVE DATABASE OF LEADING PLAYERS

9. COMPETITIVE LANDSCAPE

  • 9.1. Chapter Overview
  • 9.2. Artificial Intelligence in Life Sciences Market: Overall Market Landscape
    • 9.2.1. Analysis by Year of Establishment
    • 9.2.2. Analysis by Company Size
    • 9.2.3. Analysis by Location of Headquarters
    • 9.2.4. Analysis by Ownership Structure

10. COMPANY COMPETITIVENESS ANALYSIS

11. STARTUP ECOSYSTEM IN THE ARTIFICIAL INTELLIGENCE IN LIFE SCIENCES MARKET

  • 11.1. Artificial Intelligence in Life Sciences Market: Market Landscape of Startups
    • 11.1.1. Analysis by Year of Establishment
    • 11.1.2. Analysis by Company Size
    • 11.1.3. Analysis by Company Size and Year of Establishment
    • 11.1.4. Analysis by Location of Headquarters
    • 11.1.5. Analysis by Company Size and Location of Headquarters
    • 11.1.6. Analysis by Ownership Structure
  • 11.2. Key Findings

SECTION IV: COMPANY PROFILES

12. COMPANY PROFILES

  • 12.1. Chapter Overview
  • 12.2. Atomwise*
    • 12.2.1. Company Overview
    • 12.2.2. Company Mission
    • 12.2.3. Company Footprint
    • 12.2.4. Management Team
    • 12.2.5. Contact Details
    • 12.2.6. Financial Performance
    • 12.2.7. Operating Business Segments
    • 12.2.8. Service / Product Portfolio (project specific)
    • 12.2.9. MOAT Analysis
    • 12.2.10. Recent Developments and Future Outlook
  • 12.3. BenevolentAI
  • 12.4. Exscientia
  • 12.5. Foundation Medicine
  • 12.6. GE HealthCare
  • 12.7. IBM
  • 12.8. Insilico Medicine
  • 12.9. Microsoft
  • 12.10. NVIDIA
  • 12.11. Owkin
  • 12.12. PathAI
  • 12.12. Recursion
  • 12.14. Schrodinger
  • 12.15. Tempus AI

SECTION V: MARKET TRENDS

13. MEGA TRENDS ANALYSIS

14. PATENT ANALYSIS

15. RECENT DEVELOPMENTS

  • 15.1. Chapter Overview
  • 15.2. Recent Funding
  • 15.3. Recent Partnerships
  • 15.4. Other Recent Initiatives

SECTION VI: MARKET OPPORTUNITY ANALYSIS

16. GLOBAL ARTIFICIAL INTELLIGENCE IN LIFE SCIENCES MARKET

  • 16.1. Chapter Overview
  • 16.2. Key Assumptions and Methodology
  • 16.3. Trends Disruption Impacting Market
  • 16.4. Demand Side Trends
  • 16.5. Supply Side Trends
  • 16.6. Global Artificial Intelligence in Life Sciences Market, Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 16.7. Multivariate Scenario Analysis
    • 16.7.1. Conservative Scenario
    • 16.7.2. Optimistic Scenario
  • 16.8. Investment Feasibility Index
  • 16.9. Key Market Segmentations

17. MARKET OPPORTUNITIES BASED ON DEPLOYMENT MODE

  • 17.1. Chapter Overview
  • 17.2. Key Assumptions and Methodology
  • 17.3. Revenue Shift Analysis
  • 17.4. Market Movement Analysis
  • 17.5. Penetration-Growth (P-G) Matrix
  • 17.6. Artificial Intelligence in Life Sciences Market for Cloud: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 17.7. Artificial Intelligence in Life Sciences Market for On Premise: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 17.8. Data Triangulation and Validation
    • 17.8.1. Secondary Sources
    • 17.8.2. Primary Sources
    • 17.8.3. Statistical Modeling

18. MARKET OPPORTUNITIES BASED ON TYPE OF OFFERING

  • 18.1. Chapter Overview
  • 18.2. Key Assumptions and Methodology
  • 18.3. Revenue Shift Analysis
  • 18.4. Market Movement Analysis
  • 18.5. Penetration-Growth (P-G) Matrix
  • 18.6. Artificial Intelligence in Life Sciences Market for Software: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 18.7. Artificial Intelligence in Life Sciences Market for Hardware: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 18.8. Artificial Intelligence in Life Sciences Market for Services: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 18.9. Data Triangulation and Validation
    • 18.9.1. Secondary Sources
    • 18.9.2. Primary Sources
    • 18.9.3. Statistical Modeling

19. MARKET OPPORTUNITIES BASED ON TYPE OF TECHNOLOGY

  • 19.1. Chapter Overview
  • 19.2. Key Assumptions and Methodology
  • 19.3. Revenue Shift Analysis
  • 19.4. Market Movement Analysis
  • 19.5. Penetration-Growth (P-G) Matrix
  • 19.6. Artificial Intelligence in Life Sciences Market for Machine Learning: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 19.7. Artificial Intelligence in Life Sciences Market for Computer Vision: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 19.8. Artificial Intelligence in Life Sciences Market for Natural Language Processing: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 19.9. Artificial Intelligence in Life Sciences Market for Immunology: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 19.10. Artificial Intelligence in Life Sciences Market for Predictive Analytics: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 19.11. Data Triangulation and Validation
    • 19.11.1. Secondary Sources
    • 19.11.2. Primary Sources
    • 19.11.3. Statistical Modeling

20. MARKET OPPORTUNITIES BASED ON APPLICATION AREAS

  • 20.1. Chapter Overview
  • 20.2. Key Assumptions and Methodology
  • 20.3. Revenue Shift Analysis
  • 20.4. Market Movement Analysis
  • 20.5. Penetration-Growth (P-G) Matrix
  • 20.6. Artificial Intelligence in Life Sciences Market for Medical Diagnosis: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 20.7. Artificial Intelligence in Life Sciences Market for Drug Discovery: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 20.8. Artificial Intelligence in Life Sciences Market for Precision & Personalized Medicine: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 20.9. Artificial Intelligence in Life Sciences Market for Biotechnology: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 20.10. Artificial Intelligence in Life Sciences Market for Clinical Trials: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 20.11. Artificial Intelligence in Life Sciences Market for Patent Monitoring: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 20.12. Data Triangulation and Validation
    • 20.12.1. Secondary Sources
    • 20.12.2. Primary Sources
    • 20.12.3. Statistical Modeling

21. MARKET OPPORTUNITIES FOR ARTIFICIAL INTELLIGENCE IN LIFE SCIENCES MARKET IN NORTH AMERICA

  • 21.1. Chapter Overview
  • 21.2. Key Assumptions and Methodology
  • 21.3. Revenue Shift Analysis
  • 21.4. Market Movement Analysis
  • 21.5. Penetration-Growth (P-G) Matrix
  • 21.6. Artificial Intelligence in Life Sciences Market in North America: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 21.6.1. Artificial Intelligence in Life Sciences Market in the US: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 21.6.2. Artificial Intelligence in Life Sciences Market in Canada: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 21.6.3. Artificial Intelligence in Life Sciences Market in Mexico: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 21.6.4. Artificial Intelligence in Life Sciences Market in Other North American Countries: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 21.7. Data Triangulation and Validation

22. MARKET OPPORTUNITIES FOR ARTIFICIAL INTELLIGENCE IN LIFE SCIENCES MARKET IN EUROPE

  • 22.1. Chapter Overview
  • 22.2. Key Assumptions and Methodology
  • 22.3. Revenue Shift Analysis
  • 22.4. Market Movement Analysis
  • 22.5. Penetration-Growth (P-G) Matrix
  • 22.6. Artificial Intelligence in Life Sciences Market in Europe: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 22.6.1. Artificial Intelligence in Life Sciences Market in Austria: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 22.6.2. Artificial Intelligence in Life Sciences Market in Belgium: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 22.6.3. Artificial Intelligence in Life Sciences Market in Denmark: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 22.6.4. Artificial Intelligence in Life Sciences Market in France: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 22.6.5. Artificial Intelligence in Life Sciences Market in Germany: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 22.6.6. Artificial Intelligence in Life Sciences Market in Ireland: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 22.6.7. Artificial Intelligence in Life Sciences Market in Italy: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 22.6.8. Artificial Intelligence in Life Sciences Market in Netherlands: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 22.6.9. Artificial Intelligence in Life Sciences Market in Norway: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 22.6.10. Artificial Intelligence in Life Sciences Market in Russia: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 22.6.11. Artificial Intelligence in Life Sciences Market in Spain: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 22.6.12. Artificial Intelligence in Life Sciences Market in Sweden: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 22.6.13. Artificial Intelligence in Life Sciences Market in Switzerland: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 22.6.14. Artificial Intelligence in Life Sciences Market in the UK: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 22.6.15. Artificial Intelligence in Life Sciences Market in Other European Countries: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 22.7. Data Triangulation and Validation

23. MARKET OPPORTUNITIES FOR ARTIFICIAL INTELLIGENCE IN LIFE SCIENCES MARKET IN ASIA

  • 23.1. Chapter Overview
  • 23.2. Key Assumptions and Methodology
  • 23.3. Revenue Shift Analysis
  • 23.4. Market Movement Analysis
  • 23.5. Penetration-Growth (P-G) Matrix
  • 23.6. Artificial Intelligence in Life Sciences Market in Asia: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 23.6.1. Artificial Intelligence in Life Sciences Market in China: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 23.6.2. Artificial Intelligence in Life Sciences Market in India: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 23.6.3. Artificial Intelligence in Life Sciences Market in Japan: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 23.6.4. Artificial Intelligence in Life Sciences Market in Singapore: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 23.6.5. Artificial Intelligence in Life Sciences Market in South Korea: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 23.6.6. Artificial Intelligence in Life Sciences Market in Other Asian Countries: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 23.7. Data Triangulation and Validation

24. MARKET OPPORTUNITIES FOR ARTIFICIAL INTELLIGENCE IN LIFE SCIENCES MARKET IN MIDDLE EAST AND NORTH AFRICA (MENA)

  • 24.1. Chapter Overview
  • 24.2. Key Assumptions and Methodology
  • 24.3. Revenue Shift Analysis
  • 24.4. Market Movement Analysis
  • 24.5. Penetration-Growth (P-G) Matrix
  • 24.6. Artificial Intelligence in Life Sciences Market in Middle East and North Africa (MENA): Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 24.6.1. Artificial Intelligence in Life Sciences Market in Egypt: Historical Trends (Since 2020) and Forecasted Estimates (Till 205)
    • 24.6.2. Artificial Intelligence in Life Sciences Market in Iran: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 24.6.3. Artificial Intelligence in Life Sciences Market in Iraq: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 24.6.4. Artificial Intelligence in Life Sciences Market in Israel: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 24.6.5. Artificial Intelligence in Life Sciences Market in Kuwait: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 24.6.6. Artificial Intelligence in Life Sciences Market in Saudi Arabia: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 24.6.7. Artificial Intelligence in Life Sciences Market in United Arab Emirates (UAE): Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 24.6.8. Artificial Intelligence in Life Sciences Market in Other MENA Countries: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 24.7. Data Triangulation and Validation

25. MARKET OPPORTUNITIES FOR ARTIFICIAL INTELLIGENCE IN LIFE SCIENCES MARKET IN LATIN AMERICA

  • 25.1. Chapter Overview
  • 25.2. Key Assumptions and Methodology
  • 25.3. Revenue Shift Analysis
  • 25.4. Market Movement Analysis
  • 25.5. Penetration-Growth (P-G) Matrix
  • 25.6. Artificial Intelligence in Life Sciences Market in Latin America: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 25.6.1. Artificial Intelligence in Life Sciences Market in Argentina: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 25.6.2. Artificial Intelligence in Life Sciences Market in Brazil: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 25.6.3. Artificial Intelligence in Life Sciences Market in Chile: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 25.6.4. Artificial Intelligence in Life Sciences Market in Colombia Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 25.6.5. Artificial Intelligence in Life Sciences Market in Venezuela: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 25.6.6. Artificial Intelligence in Life Sciences Market in Other Latin American Countries: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
  • 25.7. Data Triangulation and Validation

26. MARKET OPPORTUNITIES FOR ARTIFICIAL INTELLIGENCE IN LIFE SCIENCES MARKET IN REST OF THE WORLD

  • 26.1. Chapter Overview
  • 26.2. Key Assumptions and Methodology
  • 26.3. Revenue Shift Analysis
  • 26.4. Market Movement Analysis
  • 26.5. Penetration-Growth (P-G) Matrix
  • 26.6. Artificial Intelligence in Life Sciences Market in Rest of the World: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 26.6.1. Artificial Intelligence in Life Sciences Market in Australia: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 26.6.2. Artificial Intelligence in Life Sciences Market in New Zealand: Historical Trends (Since 2020) and Forecasted Estimates (Till 2040)
    • 26.6.3. Artificial Intelligence in Life Sciences Market in Other Countries
  • 26.7. Data Triangulation and Validation

27. MARKET CONCENTRATION ANALYSIS: DISTRIBUTION BY LEADING PLAYERS

28. ADJACENT MARKET ANALYSIS

SECTION VII: STRATEGIC TOOLS

29. KEY WINNING STRATEGIES

30. PORTER'S FIVE FORCES ANALYSIS

31. SWOT ANALYSIS

32. ROOTS STRATEGIC RECOMMENDATIONS

  • 32.1. Chapter Overview
  • 32.2. Key Business-related Strategies
    • 32.2.1. Research & Development
    • 32.2.2. Product Manufacturing
    • 32.2.3. Commercialization / Go-to-Market
    • 32.2.4. Sales and Marketing
  • 32.3. Key Operations-related Strategies
    • 32.3.1. Risk Management
    • 32.3.2. Workforce
    • 32.3.3. Finance
    • 32.3.4. Others

SECTION VIII: OTHER EXCLUSIVE INSIGHTS

33. INSIGHTS FROM PRIMARY RESEARCH

34. REPORT CONCLUSION

SECTION IX: APPENDIX

35. TABULATED DATA

36. LIST OF COMPANIES AND ORGANIZATIONS

37. ROOTS SUBSCRIPTION SERVICES

38. AUTHOR DETAILS