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臨床實驗人工智能

市場調查報告書

商品編碼

1535897

臨床試驗中的人工智慧市場規模- 按組件（軟體、服務）、按技術（機器學習(ML)、自然語言處理(NLP)、電腦視覺、情境機器人）、按應用、按最終用戶和預測，2024 - 2032 年

AI in Clinical Trials Market Size - By Component (Software, Service), By Technology (Machine Learning (ML), Natural Language Processing (NLP), Computer Vision, Contextual Bots), By Application, By End User & Forecast, 2024 - 2032

出版日期: 2024年06月21日 | 出版商:

Global Market Insights Inc. | 英文 270 Pages | 商品交期: 2-3個工作天內

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簡介目錄

在人工智慧技術和個人化醫療不斷進步的推動下，2024 年至 2032 年間，全球人工智慧臨床試驗市場規模將達到 14% 的複合年成長率。增強的人工智慧演算法可以實現更準確的資料分析、更快的藥物開發並提高試驗效率。此外，個人化醫療的興起允許根據個別患者資料制定客製化治療計劃，最佳化治療結果。隨著人工智慧技術的發展和個人化方法變得更加普遍，它們的綜合影響將加速臨床試驗，使其更有效率和有效，從而擴大市場。

例如，2023 年 11 月，阿斯特捷利康推出 Evinova，將人工智慧和數位健康解決方案整合到臨床試驗中，針對 CRO、試驗申辦者、照護團隊和患者，利用該公司已在全球使用的技術。此舉顯示將先進技術融入臨床研究、推動創新、改善試驗結果以及潛在增加市場採用的趨勢不斷上升。隨著阿斯特捷利康等主要參與者採用人工智慧，這凸顯了人工智慧在改變臨床試驗流程和結果方面的不斷擴大的作用。

臨床試驗產業中的人工智慧根據組件、技術、應用、最終用戶和地區進行細分。

機器學習領域將在 2024 年至 2032 年期間大幅成長。機器學習演算法在處理大量臨床資料、識別模式和預測試驗結果方面發揮關鍵作用。這些功能顯著減少了資料分析所需的時間並提高了決策的準確性。製藥公司擴大利用機器學習來最佳化患者選擇、監控試驗進度並確保符合監管標準。將機器學習整合到臨床試驗中可以提高效率並提高試驗的整體質量，支持市場擴張。

到 2032 年，藥物發現領域將獲得可觀的效益，這歸因於其對加速藥物開發進程的變革性影響。人工智慧技術增強了預測模型，最佳化了臨床試驗設計，並更有效地識別潛在的候選藥物，從而大大減少了時間和成本。先進的演算法分析大量資料集，以發現新的藥物交互作用和生物標記，從而產生更有針對性的治療方法。隨著製藥公司擴大採用人工智慧來簡化發現和提高成功率，這一領域將佔據相當大的市場佔有率。

亞太地區人工智慧臨床試驗市場將在2024年至2032年實現適度的複合年成長率。有利的環境。由於改善醫療保健結果和解決日益嚴重的慢性病負擔的需要，中國、印度和日本等國家處於將人工智慧技術融入臨床試驗的最前沿。大量患者群體的存在和先進技術解決方案的可用性進一步支持亞太地區人工智慧在臨床試驗行業的擴張。

產業生態系統分析
供應商格局
- 平台提供者
- 軟體供應商
- 服務商
- 配銷通路
- 最終用戶
利潤率分析
技術與創新格局
專利分析
重要新聞和舉措
監管環境
衝擊力
- 成長動力
  - 加速藥物開發和發現
  - 改善病患招募
  - 增強的資料分析和即時監控
  - 個人化醫療的需求不斷成長
- 產業陷阱與挑戰
  - 資料隱私和安全問題
  - 與現有系統整合
成長潛力分析
波特的分析
PESTEL分析

第 4 章：競爭格局

介紹
公司市佔率分析
競爭定位矩陣
戰略展望矩陣

第 5 章：市場估計與預測：按組成部分，2021 - 2032 年

主要趨勢
軟體
- 第一階段
- 第二階段
- 第三階段
服務
- 第一階段
- 第二階段
- 第三階段

第 6 章：市場估計與預測：依技術分類，2021 - 2032

主要趨勢
機器學習
自然語言處理（NLP）
電腦視覺
情境機器人
其他

第 7 章：市場估計與預測：依應用分類，2021 - 2032

主要趨勢
藥物開發
藥物發現
臨床試驗管理
- 病患招募
- 臨床試驗監測
- 臨床資料管理
- 基於風險的監控
其他

第 8 章：市場估計與預測：按最終用戶分類，2021 - 2032 年

主要趨勢
製藥和生物技術公司
合約研究組織 (CRO)
學術及研究機構
其他

第 9 章：市場估計與預測：按地區，2021 - 2032

主要趨勢
北美洲
- 美國
- 加拿大
歐洲
- 英國
- 德國
- 法國
- 義大利
- 西班牙
- 俄羅斯
- 北歐人
- 歐洲其他地區
亞太地區
- 中國
- 印度
- 日本
- 澳洲
- 韓國
- 東南亞
- 亞太地區其他地區
拉丁美洲
- 巴西
- 墨西哥
- 阿根廷
- 拉丁美洲其他地區
MEA
- 阿拉伯聯合大公國
- 南非
- 沙烏地阿拉伯
- MEA 的其餘部分

第 10 章：公司簡介

BenevolentAI Ltd.
ConcertAI, Inc.
Exscientia Ltd.
GNS Healthcare
Halo Health Systems
IBM (International Business Machines Corporation)
Insilico Medicine, Inc.
IQVIA Holdings Inc.
Medidata Solutions, Inc.
Nuance Communications, Inc.
Numerate
NVIDIA Corporation
Owkin Inc.
Parexel International Corporation
Prometheus Biosciences Inc.
Renalytix AI plc
ReviveMed Ltd.
Saama Technologies, Inc.
Sensyne Health plc
TrialTrove Inc.

簡介目錄

Product Code: 9965

Global AI in Clinical Trials Market size will capture a 14% CAGR between 2024 and 2032, driven by continuous advancements in AI technologies and personalized medicine. Enhanced AI algorithms enable more accurate data analysis, faster drug development, and improved trial efficiency. Also, the rise of personalized medicine allows for tailored treatment plans based on individual patient data, optimizing therapeutic outcomes. As AI technologies evolve and personalized approaches become more prevalent, their combined impact will accelerate clinical trials, making them more efficient and effective, thereby expanding the market.

For instance, in November 2023, AstraZeneca introduced Evinova to integrate AI and digital health solutions into clinical trials, targeting CROs, trial sponsors, care teams, and patients, leveraging technologies already used globally by the company. This move indicates a rising trend towards integrating advanced technologies in clinical research, driving innovation, improving trial outcomes, and potentially increasing market adoption. As major players like AstraZeneca adopt AI, it underscores the expanding role of AI in transforming clinical trial processes and outcomes.

The AI in clinical trials industry is segmented based on component, technology, application, end-user, and region.

The machine learning segment will witness substantial growth throughout 2024-2032. Machine learning algorithms play a pivotal role in processing vast amounts of clinical data, identifying patterns, and predicting trial outcomes. These capabilities significantly reduce the time required for data analysis and enhance decision-making accuracy. Pharmaceutical companies are increasingly leveraging machine learning to optimize patient selection, monitor trial progress, and ensure compliance with regulatory standards. The integration of machine learning in clinical trials improves efficiency and enhances the overall quality of trials, supporting market expansion.

The drug discovery segment will amass considerable gains by 2032, attributed to its transformative impact on accelerating drug development processes. AI technologies enhance predictive modeling, optimize clinical trial designs, and identify potential drug candidates more efficiently, considerably reducing time and costs. Advanced algorithms analyze vast datasets to uncover novel drug interactions and biomarkers, leading to more targeted therapies. As pharmaceutical companies increasingly adopt AI for its ability to streamline discovery and improve success rates, this segment will hold a decent market share.

Asia Pacific AI in clinical trials market will achieve a moderate CAGR from 2024 to 2032. The region's rapidly evolving healthcare infrastructure, increasing investment in medical research, and supportive government policies create a conducive environment for AI adoption. Countries like China, India, and Japan are at the forefront of integrating AI technologies into clinical trials, driven by the need to improve healthcare outcomes and address the growing burden of chronic diseases. The presence of a large patient population and the availability of advanced technological solutions further support the expansion of the Asia Pacific AI in clinical trials industry.

Chapter 1 Methodology & Scope

1.1 Market scope & definition
1.2 Research design
- 1.2.1 Research approach
- 1.2.2 Data collection methods
1.3 Base estimates & calculations
- 1.3.1 Base year calculation
- 1.3.2 Key trends for market estimation
1.4 Forecast model
1.5 Primary research and validation
- 1.5.1 Primary sources
- 1.5.2 Data mining sources

Chapter 2 Executive Summary

2.1 Industry360°synopsis, 2021 - 2032

Chapter 3 Industry Insights

3.1 Industry ecosystem analysis
3.2 Supplier landscape
- 3.2.1 Platform provider
- 3.2.2 Software provider
- 3.2.3 Service provider
- 3.2.4 Distribution channel
- 3.2.5 End user
3.3 Profit margin analysis
3.4 Technology & innovation landscape
3.5 Patent analysis
3.6 Key news & initiatives
3.7 Regulatory landscape
3.8 Impact forces
- 3.8.1 Growth drivers
  - 3.8.1.1 Accelerated drug development and discovery
  - 3.8.1.2 Improved patient recruitment
  - 3.8.1.3 Enhanced data analysis and real time monitoring
  - 3.8.1.4 Rising need of personalized medicine
- 3.8.2 Industry pitfalls & challenges
  - 3.8.2.1 Data privacy and security concerns
  - 3.8.2.2 Integration with existing systems
3.9 Growth potential analysis
3.10 Porter's analysis
- 3.10.1 Supplier power
- 3.10.2 Buyer power
- 3.10.3 Threat of new entrants
- 3.10.4 Threat of substitutes
- 3.10.5 Industry rivalry
3.11 PESTEL analysis

Chapter 4 Competitive Landscape, 2023

4.1 Introduction
4.2 Company market share analysis
4.3 Competitive positioning matrix
4.4 Strategic outlook matrix

Chapter 5 Market Estimates & Forecast, By Component, 2021 - 2032 ($Bn)

5.1 Key trends
5.2 Software
- 5.2.1 Phase I
- 5.2.2 Phase II
- 5.2.3 Phase III
5.3 Service
- 5.3.1 Phase I
- 5.3.2 Phase II
- 5.3.3 Phase III

Chapter 6 Market Estimates & Forecast, By Technology, 2021 - 2032 ($Bn)

6.1 Key trends
6.2 Machine learning
6.3 Natural Language Processing (NLP)
6.4 Computer vision
6.5 Contextual bots
6.6 Others

Chapter 7 Market Estimates & Forecast, By Application, 2021 - 2032 ($Bn)

7.1 Key trends
7.2 Drug development
7.3 Drug discovery
7.4 Clinical trial management
- 7.4.1 Patient recruitment
- 7.4.2 Clinical trial monitoring
- 7.4.3 Clinical data management
- 7.4.4 Risk-based monitoring
7.5 Others

Chapter 8 Market Estimates & Forecast, By End User, 2021 - 2032 ($Bn)

8.1 Key trends
8.2 Pharmaceutical and biotechnology companies
8.3 Contract Research Organizations (CROs)
8.4 Academic and research institutes
8.5 Others

Chapter 9 Market Estimates & Forecast, By Region, 2021 - 2032 ($Bn)

9.1 Key trends
9.2 North America
- 9.2.1 U.S.
- 9.2.2 Canada
9.3 Europe
- 9.3.1 UK
- 9.3.2 Germany
- 9.3.3 France
- 9.3.4 Italy
- 9.3.5 Spain
- 9.3.6 Russia
- 9.3.7 Nordics
- 9.3.8 Rest of Europe
9.4 Asia Pacific
- 9.4.1 China
- 9.4.2 India
- 9.4.3 Japan
- 9.4.4 Australia
- 9.4.5 South Korea
- 9.4.6 Southeast Asia
- 9.4.7 Rest of Asia Pacific
9.5 Latin America
- 9.5.1 Brazil
- 9.5.2 Mexico
- 9.5.3 Argentina
- 9.5.4 Rest of Latin America
9.6 MEA
- 9.6.1 UAE
- 9.6.2 South Africa
- 9.6.3 Saudi Arabia
- 9.6.4 Rest of MEA