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市場調查報告書
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1958431

醫療人工智慧市場（至2040年）：依組件、技術、應用、最終用戶和主要地區劃分－產業趨勢和全球預測

AI in Medicine Market, till 2040: Distribution by Type of Component, Type of Technology, Application, Type of End User and Key Geographical Regions: Industry Trends and Global Forecasts
出版日期: | 出版商: Roots Analysis | 英文 189 Pages | 商品交期: 7-10個工作天內

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簡介目錄

全球醫療人工智慧市場預計將從目前的292.7億美元成長到2040年的33,653.8億美元，預測期內複合年增長率（CAGR）預計為40.34%。

人工智慧正在透過整合先進的演算法、機器學習和深度神經網絡，革新醫療保健產業，從而提升整個醫療保健生態系統的診斷水平、個人化治療和營運效率。從藥物研發中的預測分析到分析基因組數據以實現個人化治療的精準醫療應用，人工智慧正在以前所未有的精確度和速度識別疾病。在醫療器材領域，人工智慧驅動的可穿戴設備和診斷成像工具能夠實現即時監測和早期幹預，近期研究表明，它們可以減少放射學誤診。

受全球慢性病和遺傳疾病負擔日益加重，以及對個人化治療需求不斷增長的推動，醫療人工智慧市場正經歷強勁增長。此外，人們越來越依賴人工智慧驅動的精準診斷和治療，這進一步促進了市場擴張，因為人工智慧在分析複雜的生物數據方面非常有效。同時，主要公司加強研發活動和策略性產品發布也是推動市場成長的重要因素。

AI in Medicine Market-IMG1

推動醫療人工智慧市場成長的關鍵因素

包括癌症、糖尿病和心血管疾病在內的慢性病和遺傳患者病率不斷上升，推動了對醫療人工智慧的需求，醫療人工智慧能夠實現更精準的診斷、個人化的治療方案和預測性醫療。 具體而言，人工智慧演算法能夠分析大量的基因組、臨床、生活方式和分子數據，從而發現疾病模式、基因突變和治療標靶。同時，市場也受到個人化醫療和精準診斷需求不斷增長的推動。人工智慧能夠實現個人化治療策略、預測疾病進展、選擇最佳治療方案並最大限度地減少副作用。這些功能與市場對數據驅動型解決方案的需求相契合，這些解決方案旨在提高準確性和臨床療效。 此外，全球產品研發活動的增加正在加速醫療人工智慧市場的創新。這些活動包括企業和研究機構對先進人工智慧演算法、診斷工具和藥物發現平台的投資。這些努力推動了生物標記的識別、數據處理和治療方案的客製化，從而促進了市場擴張和人工智慧在臨床環境中的應用。

醫療人工智慧市場：主要市場區隔

組成部分

  • 硬體
  • 軟體
  • 服務

科技

  • 自然語言處理
  • 機器學習
  • 電腦視覺

應用

  • 藥物研發
  • 臨床研究試驗
  • 個人化醫療
  • 其他

最終使用者

  • 醫院和診所
  • 製藥和生技公司
  • 診斷實驗室
  • 其他

主要地區

  • 北美
  • 美國美國
  • 加拿大
  • 墨西哥
  • 其他北美國家
  • 歐洲
  • 奧地利
  • 比利時
  • 丹麥
  • 法國
  • 德國
  • 奧地利愛爾蘭
  • 義大利
  • 荷蘭
  • 挪威
  • 俄羅斯
  • 西班牙
  • 瑞典
  • 瑞士
  • 英國
  • 其他歐洲國家
  • 亞洲
  • 中國
  • 印度
  • 日本
  • 新加坡
  • 韓國
  • 其他亞洲國家
  • 拉丁美洲
  • 巴西
  • 智利
  • 哥倫比亞
  • 委內瑞拉
  • 其他拉丁美洲國家
  • 中東和北非非洲
  • 埃及
  • 伊朗
  • 伊拉克
  • 以色列
  • 科威特
  • 沙烏地阿拉伯
  • 阿拉伯聯合大公國
  • 其他中東和北非國家
  • 世界其他地區
  • 澳大利亞
  • 紐西蘭
  • 其他國家

本報告分析了全球醫療人工智慧市場，並提供了市場概況、背景、市場影響因素分析、市場規模趨勢和預測、依不同細分市場和地區進行的詳細分析、競爭格局以及主要公司的簡介。

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：質性分析

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

第九章：競爭格局

第十章：空白市場分析

第十一章：競爭分析

第十二章：醫療人工智慧市場的新創企業生態系統

第四部分：公司簡介

第十三章：公司簡介

  • 章節概述
  • AiCure
  • Atomwise
  • Berg
  • Cyrcadia Health
  • Medasense Biometrics
  • Modernizing Medicine
  • Nano-X Imaging
  • Novo諾德
  • Sense.ly
  • 歐金
  • 路徑AI
  • Qure.ai
  • 坦帕斯

第五部分：市場趨勢

第 14 章：大趨勢分析

第 15 章：專利分析

第 16 章：最新進展

第六節：市場機會分析

第 17 章：全球醫療人工智慧市場

第 18 章：依組件劃分的市場機會

第 19 章：科技帶來的市場機會

章節

第20章：依應用領域劃分的市場機會

第21章：依最終用戶劃分的市場機會

第22章：北美醫療人工智慧市場機會

第23章：歐洲醫療人工智慧市場機會

第24章：亞洲醫療人工智慧市場機會

第25章：中東及北非醫療人工智慧市場機會

第26章：拉丁美洲醫療人工智慧市場機會

第27章：世界其他地區醫療人工智慧市場機會

第28章：市場集中度分析：主要參與者分佈

第29章：鄰近市場

分析

第七部分：策略工具

第30章：關鍵制勝策略

第31章：波特五力分析

第32章：SWOT分析

第33章：ROOTS策略建議

第八部分：其他獨家見解

第34章：來自一手研究的見解

第35章：報告結論

第九部分：附錄

第36章：表格資料

第37章：公司列表與組織機構

第38章：ROOTS訂閱服務

第39章：作者詳情

簡介目錄
Product Code: RAD00036

AI in medicine Market Outlook

As per Roots Analysis, the global AI in medicine market size is estimated to grow from USD 29.27 billion in current year to USD 3,365.38 billion by 2040, at a CAGR of 40.34% during the forecast period, till 2040.

Artificial Intelligence (AI) is revolutionizing medicine by integrating advanced algorithms, machine learning, and deep neural networks to enhance diagnostics, treatment personalization, and operational efficiency across healthcare ecosystems. From predictive analytics in drug discovery, to precision medicine applications that analyze genomic data for tailored therapies, AI drives unprecedented accuracy and speed in identifying diseases. In medical devices, AI-powered wearables and imaging tools enable real-time monitoring and early intervention, reducing diagnostic errors in radiology, as per recent studies.

The AI in medicine market is experiencing robust growth, fueled by the escalating global burden of chronic and genetic diseases, which heightens demand for personalized therapies. This expansion is further fueled by the growing reliance on AI-driven precision diagnostics and therapeutics, which are highly effective at analyzing complex biological data. Moreover, increased research and development efforts, along with strategic launches from leading companies, are propelling market growth.

AI in Medicine Market - IMG1

Strategic Insights for Senior Leaders

Key Drivers Propelling Growth of AI in Medicine Market

The rising prevalence of chronic and genetic diseases including cancer, diabetes, and cardiovascular disorders, is driving the demand for AI in medicine. This enables more accurate diagnostics, personalized treatment planning, and predictive healthcare. Notably, AI algorithms analyze extensive genomic, clinical, lifestyle, and molecular datasets to uncover disease patterns, genetic mutations, and therapeutic targets. Concurrently, the rising demand for personalized medicines and precision diagnostics is bolstering the market. AI facilitates individualized treatment strategies, disease progression forecasting, optimal therapy selection, and adverse effect minimization. These capabilities align with preferences for data-driven solutions that enhance accuracy and clinical outcomes.

Further, rising global product development activities are accelerating innovation in the AI in medicine market. These activities include investments from companies and research institutions in advanced AI algorithms, diagnostic tools, and drug discovery platforms. Such efforts propel biomarker identification, data processing, and treatment customization, thereby driving market expansion and AI adoption in clinical practice.

AI in Medicine Market: Competitive Landscape of Companies in this Industry

The competitive landscape in AI for medicine features a mix of established technology giants, specialized AI startups, and biotech firms. Leaders like Google DeepMind, IBM Watson Health, NVIDIA, Tempus, and PathAI dominate through substantial investments in machine learning algorithms for diagnostics, drug discovery, and personalized treatment. These companies leverage proprietary datasets, cloud-based platforms, and strategic partnerships with pharmaceutical giants like Pfizer and Roche to accelerate AI-driven medicine solutions.

Emerging challengers, including Insilico Medicine and BenevolentAI intensify competition by focusing on generative AI for novel drug design, while regulatory advancements from the FDA and EMA foster consolidation through mergers and acquisitions.

AI in Medicine Evolution: Emerging Trends in the Industry

Emerging trends in AI in medicine sector include generative AI for automated clinical documentation, AI-powered remote patient monitoring through wearable devices, natural language processing (NLP) for electronic health records (EHR) extraction, AI-accelerated drug discovery, and predictive analytics for early disease detection. These innovations enhance operational efficiency, enable personalized care pathways, and optimize patient outcomes by leveraging vast datasets for diagnostics, treatment planning, and workflow automation. Further, key growth areas encompass the Internet of Medical Things (IoMT), mental health interventions, and AI-driven clinical trials, though persistent challenges in data privacy, regulatory compliance, and algorithmic bias necessitate robust governance frameworks.

Impact of US Tariff on Artificial Intelligence (AI) in Medicine Market

US tariffs are creating supply chain challenges for AI in medicine market. These primarily include raising costs on imported AI hardware, medical components, and pharmaceuticals from key regions like China and Europe. Such measures disrupt global R&D collaborations and data processing tools essential for genomic analysis and personalized therapies. This prompts firms to regionalize operations and invest in domestic AI infrastructure. Early surveys indicate minimal direct financial impact on life sciences companies so far. However, ongoing trade tensions could delay biomarker discovery platforms and inflate development expenses for AI-driven diagnostics.

Pharma leaders anticipate AI efficiencies to help alleviate certain pressures, with opportunities emerging for US based innovators in clinical trials and smart manufacturing. Adaptation strategies, including automation and localized supply chains, will be critical to sustaining precision medicine advancements amid these economic shifts.

Key Market Challenges

The AI in medicine market faces several key challenges that hinder widespread adoption. One of the primary challenges include data-related issues, including privacy constraints under General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPAA), inconsistent data quality, limited access to diverse datasets, and inherent biases. Additional barriers include difficulties in integrating AI solutions with traditional healthcare systems and challenges in substantiating clinical efficacy through rigorous validation. Addressing these challenges necessitates cultural shifts within healthcare organizations, along with the implementation of robust governance frameworks and explainable AI techniques.

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

According to our estimates North America currently captures a significant share of the AI in medicine market. This can be attributed to surging chronic disease burdens, 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.

AI in Medicine Market: Key Market Segmentation

Type of Component

  • Hardware
  • Software
  • Service

Type of Technology

  • Natural Language Processing
  • Machine Learning
  • Computer Vision

Application

  • Drug Discovery
  • Clinical Research Trial
  • Personalized Medicine
  • Others

Type of End User

  • Hospitals and Clinics
  • Pharmaceutical and Biotech Firms
  • Diagnostic Laboratories
  • Others

Key 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
  • Australia
  • New Zealand
  • Other countries

Example Players in AI in Medicine Market

  • AiCure
  • Atomwise
  • Berg
  • Cyrcadia Health
  • Medasense Biometrics
  • Modernizing Medicine
  • Nano-X Imaging
  • Novo Nordisk
  • Sense.ly
  • Owkin
  • PathAI
  • Qure.ai
  • Tempus

AI in Medicine Market: Report Coverage

The report on the AI in medicine market features insights on various sections, including:

  • Market Sizing and Opportunity Analysis: An in-depth analysis of the AI in medicine market, focusing on key market segments, including [A] type of component, [B] type of technology, [C] application, [D] type of end user and [E] key geographical regions.
  • Competitive Landscape: A comprehensive analysis of the companies engaged in the AI in medicine 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 AI in medicine 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] portfolio, [J] recent developments, and an informed future outlook.
  • Megatrends: An evaluation of ongoing megatrends in the AI in medicine industry.
  • Recent Developments: An overview of the recent developments made in the AI in medicine 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 AI in Medicine 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. AI in Medicine 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. WHITE SPACE ANALYSIS

11. COMPANY COMPETITIVENESS ANALYSIS

12. STARTUP ECOSYSTEM IN THE AI IN MEDICINE MARKET

  • 12.1. AI in Medicine Market: Market Landscape of Startups
    • 12.1.1. Analysis by Year of Establishment
    • 12.1.2. Analysis by Company Size
    • 12.1.3. Analysis by Company Size and Year of Establishment
    • 12.1.4. Analysis by Location of Headquarters
    • 12.1.5. Analysis by Company Size and Location of Headquarters
    • 12.1.6. Analysis by Ownership Structure
  • 12.2. Key Findings

SECTION IV: COMPANY PROFILES

13. COMPANY PROFILES

  • 13.1. Chapter Overview
  • 13.2. AiCure*
    • 13.2.1. Company Overview
    • 13.2.2. Company Mission
    • 13.2.3. Company Footprint
    • 13.2.4. Management Team
    • 13.2.5. Contact Details
    • 13.2.6. Financial Performance
    • 13.2.7. Operating Business Segments
    • 13.2.8. Service / Product Portfolio (project specific)
    • 13.2.9. MOAT Analysis
    • 13.2.10. Recent Developments and Future Outlook
  • 13.3. Atomwise
  • 13.4. Berg
  • 13.5. Cyrcadia Health
  • 13.6. Medasense Biometrics
  • 13.7. Modernizing Medicine
  • 13.8. Nano-X Imaging
  • 13.9. Novo Nordisk
  • 13.10. Sense.ly
  • 13.11. Owkin
  • 13.12. PathAI
  • 13.13. Qure.ai
  • 13.14. Tempus

SECTION V: MARKET TRENDS

14. MEGA TRENDS ANALYSIS

15. PATENT ANALYSIS

16. RECENT DEVELOPMENTS

  • 16.1. Chapter Overview
  • 16.2. Recent Funding
  • 16.3. Recent Partnerships
  • 16.4. Other Recent Initiatives

SECTION VI: MARKET OPPORTUNITY ANALYSIS

17. GLOBAL AI IN MEDICINE MARKET

  • 17.1. Chapter Overview
  • 17.2. Key Assumptions and Methodology
  • 17.3. Trends Disruption Impacting Market
  • 17.4. Demand Side Trends
  • 17.5. Supply Side Trends
  • 17.6. Global AI in Medicine Market, Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
  • 17.7. Multivariate Scenario Analysis
    • 17.7.1. Conservative Scenario
    • 17.7.2. Optimistic Scenario
  • 17.8. Investment Feasibility Index
  • 17.9. Key Market Segmentations

18. MARKET OPPORTUNITIES BASED ON TYPE OF COMPONENT

  • 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. AI in Medicine Market for Hardware: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
  • 18.7. AI in Medicine Market for Software: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
  • 18.8. AI in Medicine Market for Services: Historical Trends (Since 2022) 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. AI in Medicine Market for Natural Language Processing: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
  • 19.7. AI in Medicine Market for Machine Learning: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
  • 19.8. AI in Medicine Market for Computer Vision: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
  • 19.9. Data Triangulation and Validation
    • 19.9.1. Secondary Sources
    • 19.9.2. Primary Sources
    • 19.9.3. Statistical Modeling

20. MARKET OPPORTUNITIES BASED ON APPLICATION

  • 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. AI in Medicine Market for Drug Discovery: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
  • 20.7. AI in Medicine Market for Clinical Research Trial: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
  • 20.8. AI in Medicine Market for Personalized Medicine: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
  • 20.8. AI in Medicine Market for Others: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
  • 20.8. Data Triangulation and Validation
    • 20.8.1. Secondary Sources
    • 20.8.2. Primary Sources
    • 20.8.3. Statistical Modeling

21. MARKET OPPORTUNITIES BASED ON TYPE OF END USER

  • 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. AI in Medicine Market for Hospitals and Clinics: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
  • 21.7. AI in Medicine Market for Pharmaceutical and Biotech Firms: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
  • 21.8. AI in Medicine Market for Diagnostic Laboratories: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
  • 21.8. AI in Medicine Market for Others: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
  • 21.8. Data Triangulation and Validation
    • 21.8.1. Secondary Sources
    • 21.8.2. Primary Sources
    • 21.8.3. Statistical Modeling

22. MARKET OPPORTUNITIES FOR AI IN MEDICINE MARKET IN NORTH AMERICA

  • 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. AI in Medicine Market in North America: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 22.6.1. AI in Medicine Market in the US: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 22.6.2. AI in Medicine Market in Canada: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 22.6.3. AI in Medicine Market in Mexico: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 22.6.4. AI in Medicine Market in Other North American Countries: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
  • 22.7. Data Triangulation and Validation

23. MARKET OPPORTUNITIES FOR AI IN MEDICINE MARKET IN EUROPE

  • 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. AI in Medicine Market in Europe: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 23.6.1. AI in Medicine Market in Austria: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 23.6.2. AI in Medicine Market in Belgium: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 23.6.3. AI in Medicine Market in Denmark: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 23.6.4. AI in Medicine Market in France: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 23.6.5. AI in Medicine Market in Germany: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 23.6.6. AI in Medicine Market in Ireland: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 23.6.7. AI in Medicine Market in Italy: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 23.6.8. AI in Medicine Market in Netherlands: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 23.6.9. AI in Medicine Market in Norway: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 23.6.10. AI in Medicine Market in Russia: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 23.6.11. AI in Medicine Market in Spain: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 23.6.12. AI in Medicine Market in Sweden: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 23.6.13. AI in Medicine Market in Switzerland: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 23.6.14. AI in Medicine Market in the UK: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 23.6.15. AI in Medicine Market in Other European Countries: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
  • 23.7. Data Triangulation and Validation

24. MARKET OPPORTUNITIES FOR AI IN MEDICINE MARKET IN ASIA

  • 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. AI in Medicine Market in Asia: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 24.6.1. AI in Medicine Market in China: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 24.6.2. AI in Medicine Market in India: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 24.6.3. AI in Medicine Market in Japan: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 24.6.4. AI in Medicine Market in Singapore: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 24.6.5. AI in Medicine Market in South Korea: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 24.6.6. AI in Medicine Market in Other Asian Countries: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
  • 24.7. Data Triangulation and Validation

25. MARKET OPPORTUNITIES FOR AI IN MEDICINE MARKET IN MIDDLE EAST AND NORTH AFRICA (MENA)

  • 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. AI in Medicine Market in Middle East and North Africa (MENA): Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 25.6.1. AI in Medicine Market in Egypt: Historical Trends (Since 2022) and Forecasted Estimates (Till 205)
    • 25.6.2. AI in Medicine Market in Iran: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 25.6.3. AI in Medicine Market in Iraq: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 25.6.4. AI in Medicine Market in Israel: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 25.6.5. AI in Medicine Market in Kuwait: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 25.6.6. AI in Medicine Market in Saudi Arabia: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 25.6.7. AI in Medicine Market in United Arab Emirates (UAE): Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 25.6.8. AI in Medicine Market in Other MENA Countries: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
  • 25.7. Data Triangulation and Validation

26. MARKET OPPORTUNITIES FOR AI IN MEDICINE MARKET IN LATIN AMERICA

  • 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. AI in Medicine Market in Latin America: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 26.6.1. AI in Medicine Market in Argentina: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 26.6.2. AI in Medicine Market in Brazil: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 26.6.3. AI in Medicine Market in Chile: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 26.6.4. AI in Medicine Market in Colombia Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 26.6.5. AI in Medicine Market in Venezuela: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 26.6.6. AI in Medicine Market in Other Latin American Countries: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
  • 26.7. Data Triangulation and Validation

27. MARKET OPPORTUNITIES FOR AI IN MEDICINE MARKET IN REST OF THE WORLD

  • 27.1. Chapter Overview
  • 27.2. Key Assumptions and Methodology
  • 27.3. Revenue Shift Analysis
  • 27.4. Market Movement Analysis
  • 27.5. Penetration-Growth (P-G) Matrix
  • 27.6. AI in Medicine Market in Rest of the World: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 27.6.1. AI in Medicine Market in Australia: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 27.6.2. AI in Medicine Market in New Zealand: Historical Trends (Since 2022) and Forecasted Estimates (Till 2040)
    • 27.6.3. AI in Medicine Market in Other Countries
  • 27.7. Data Triangulation and Validation

28. MARKET CONCENTRATION ANALYSIS: DISTRIBUTION BY LEADING PLAYERS

29. ADJACENT MARKET ANALYSIS

SECTION VII: STRATEGIC TOOLS

30. KEY WINNING STRATEGIES

31. PORTER'S FIVE FORCES ANALYSIS

32. SWOT ANALYSIS

33. ROOTS STRATEGIC RECOMMENDATIONS

  • 33.1. Chapter Overview
  • 33.2. Key Business-related Strategies
    • 33.2.1. Research & Development
    • 33.2.2. Product Manufacturing
    • 33.2.3. Commercialization / Go-to-Market
    • 33.2.4. Sales and Marketing
  • 33.3. Key Operations-related Strategies
    • 33.3.1. Risk Management
    • 33.3.2. Workforce
    • 33.3.3. Finance
    • 33.3.4. Others

SECTION VIII: OTHER EXCLUSIVE INSIGHTS

34. INSIGHTS FROM PRIMARY RESEARCH

35. REPORT CONCLUSION

SECTION IX: APPENDIX

36. TABULATED DATA

37. LIST OF COMPANIES AND ORGANIZATIONS

38. ROOTS SUBSCRIPTION SERVICES

39. AUTHOR DETAILS