臨床試驗中人工智慧市場規模、佔有率和成長分析：按交付方式、人工智慧技術類型、臨床試驗階段、治療領域、應用、最終用戶和地區分類——2026-2033年產業預測

2024年全球臨床試驗人工智慧市場價值為18.7億美元，預計將從2025年的21.8億美元成長到2033年的75.7億美元。預測期（2026-2033年）的複合年成長率預計為16.8%。

全球臨床試驗人工智慧市場的發展動力源於縮短研發週期和控制不斷攀升的研發成本的迫切需求，促使申辦方在整個試驗流程中實施自動化。該領域涵蓋了先進的演算法和平台，能夠增強患者識別、預測保留率、標準化終點評估和提供自適應設計支持，從而加快核准並最大限度地降低成本。從傳統生物統計學到先進機器學習的演進反映了電子健康記錄和基因組資料庫等資料來源的成熟。整合多樣化的資料流提高了模型的通用性，能夠更準確地識別佇列和預測安全性訊號，從而減少篩檢失敗並縮短入組時間。此外，人工智慧透過自動化合格評估和患者招募提高了患者招募效率，最終促進了分散式試驗的發展並刺激了投資。

全球人工智慧市場在臨床試驗中的促進因素

人工智慧在臨床試驗領域的全球市場正受到人工智慧技術快速普及的顯著推動。人工智慧能夠最佳化受試者配對、改進試驗通訊協定、最佳化研究中心選擇並簡化招募流程。這種整合最大限度地減少了延誤，提高了研究的可行性。人工智慧能夠從電子健康記錄和真實世界數據中準確識別合格的患者，從而提高入組效率並確保通訊協定的依從性。此外，先進的預測模型有助於最佳化資源分配和風險管理，促使申辦方採用人工智慧解決方案。這種營運效率的提升，加上人們對試驗品質改善的預期，正在推動人工智慧被更廣泛地接受並無縫整合到臨床開發工作流程中。

全球人工智慧市場在臨床試驗中面臨的限制因素

由於嚴格的患者隱私法規和日益成長的資料安全擔憂，全球臨床試驗領域的人工智慧市場面臨嚴峻挑戰。這些問題限制了對關鍵臨床資料集的訪問，而這些資料集對於開發有效的人工智慧模型至關重要。匿名化複雜臨床數據和確保區域合規性方面的挑戰，使得集中式資料存取變得困難，並阻礙了機構間的合作。這進一步增加了供應商的難度，並可能透過限制可用於演算法訓練的資料多樣性，影響人工智慧模型的可靠性和適用性。因此，在建立適當的隱私保護措施和管治策略之前，各機構可能會選擇推遲或限制在臨床試驗中採用人工智慧。

全球人工智慧市場在臨床試驗中的趨勢

在全球臨床試驗人工智慧市場，一個重要趨勢正在興起：將真實世界數據（RWE）整合到其框架中。人工智慧平台能夠熟練地處理各種臨床和真實世界資料來源，從而提高試驗設計、患者選擇和結果評估中使用的證據品質。這項進步使人工智慧能夠識別各種醫療保健環境和非結構化資料中的模式，從而使試驗結果與常規臨床實踐更加緊密地結合。隨著申辦者和研究人員越來越重視互通模型和可解釋的輸出，將觀察性研究結果轉化為可操作的試驗假設的趨勢日益明顯。這打破了證據孤島，提高了試驗結果在常規醫療保健中的相關性和效用。

目錄

介紹

• 調查目的
• 市場定義和範圍

調查方法

• 研究過程
• 二級資料和一級資料的方法
• 市場規模估算方法

執行摘要

• 全球市場展望
• 市場主要亮點
• 細分市場概覽
• 競爭環境概述

市場動態及展望

• 總體經濟指標
• 促進者和機會
• 抑制因素和挑戰
• 供給面趨勢
• 需求面趨勢
• 波特的分析和影響

市場考察

• 關鍵成功因素
• 影響市場的因素
• 主要投資機會
• 生態系測繪
• 2025年市場吸引力指數
• PESTEL 分析
• 監理情勢

全球人工智慧臨床試驗市場規模：依產品/服務及複合年成長率分類（2026-2033 年）

• 軟體
• 服務
• 硬體

全球人工智慧臨床試驗市場規模：按人工智慧技術類型和複合年成長率分類（2026-2033 年）

• 機器學習
  • 監督式學習
  • 無監督學習
• 深度學習
• 自然語言處理（NLP）
• 電腦視覺

全球人工智慧臨床試驗市場規模：依臨床試驗階段及複合年成長率分類（2026-2033 年）

• 第一階段
• 第二階段
• 第三階段
• 第四階段

全球人工智慧臨床試驗市場規模：按治療領域和複合年成長率分類（2026-2033 年）

• 腫瘤學
• 感染疾病
• 神經病學
• 循環系統
• 代謝性疾病
• 免疫學
• 其他

全球人工智慧臨床試驗市場規模：按應用及複合年成長率分類（2026-2033 年）

• 病人招募/留存
• 測試設計和通訊協定最佳化
• 數據管理與分析
• 監測和安全監視
• 藥物發現支持

全球人工智慧臨床試驗市場規模：按最終用戶和複合年成長率分類（2026-2033 年）

• 製藥公司
• 生技公司
• 合約研究組織（CRO）
• 學術和研究機構
• 醫院和臨床中心

全球人工智慧市場規模及臨床試驗複合年成長率（2026-2033）

• 北美洲
  • 美國
  • 加拿大
• 歐洲
  • 德國
  • 西班牙
  • 法國
  • 英國
  • 義大利
  • 其他歐洲地區
• 亞太地區
  • 中國
  • 印度
  • 日本
  • 韓國
  • 亞太其他地區
• 拉丁美洲
  • 墨西哥
  • 巴西
  • 其他拉丁美洲地區
• 中東和非洲
  • 海灣合作理事會國家
  • 南非
  • 其他中東和非洲地區

競爭資訊

• 前五大公司對比
• 主要企業的市場定位（2025 年）
• 主要市場公司採取的策略
• 近期市場趨勢
• 企業市佔率分析（2025 年）
• 主要企業公司簡介
  • 公司詳情
  • 產品系列分析
  • 按細分市場進行企業市佔率分析
  • 2023-2025年營收年比比較

主要企業簡介

• Medidata（Dassault Systmes）
• IQVIA
• Unlearn.ai
• Owkin
• Saama Technologies
• AiCure
• Deep6.ai
• Exscientia
• Antidote Technologies
• BioSymetrics
• Euretos
• Ardigen
• Mendel AI
• Phesi
• ICON plc
• Parexel
• Syneos Health
• nference
• ConcertAI
• SAS Institute

結論與建議

Global Ai In Clinical Trials Market size was valued at USD 1.87 Billion in 2024 and is poised to grow from USD 2.18 Billion in 2025 to USD 7.57 Billion by 2033, growing at a CAGR of 16.8% during the forecast period (2026-2033).

The global AI in clinical trials market is driven by the imperative to reduce development timelines and manage escalating R&D costs, leading sponsors to implement automation throughout trial processes. This sector encompasses advanced algorithms and platforms that enhance patient identification, retention predictions, endpoint assessment standardization, and adaptive design support, subsequently expediting approvals and minimizing expenditures. The evolution from traditional biostatistics to sophisticated machine learning reflects the maturation of data sources, such as electronic health records and genomic databases. The integration of diverse data streams enhances model generalizability, enabling more precise cohort identification and safety signal predictions, which decreases screening failures and enrollment periods. Additionally, AI facilitates patient recruitment efficiency by automating eligibility assessments and outreach, ultimately fostering opportunities for decentralized trials and stimulating investments.

Top-down and bottom-up approaches were used to estimate and validate the size of the Global Ai In Clinical Trials market and to estimate the size of various other dependent submarkets. The research methodology used to estimate the market size includes the following details: The key players in the market were identified through secondary research, and their market shares in the respective regions were determined through primary and secondary research. This entire procedure includes the study of the annual and financial reports of the top market players and extensive interviews for key insights from industry leaders such as CEOs, VPs, directors, and marketing executives. All percentage shares split, and breakdowns were determined using secondary sources and verified through Primary sources. All possible parameters that affect the markets covered in this research study have been accounted for, viewed in extensive detail, verified through primary research, and analyzed to get the final quantitative and qualitative data.

Global Ai In Clinical Trials Market Segments Analysis

Global ai in clinical trials market is segmented by offering, ai technology type, clinical trial phase, therapeutic area, application, end user and region. Based on offering, the market is segmented into Software, Services and Hardware. Based on ai technology type, the market is segmented into Machine Learning, Deep Learning, Natural Language Processing (NLP) and Computer Vision. Based on clinical trial phase, the market is segmented into Phase I, Phase II, Phase III and Phase IV. Based on therapeutic area, the market is segmented into Oncology, Infectious Diseases, Neurology, Cardiovascular, Metabolic Disorders, Immunology and Others. Based on application, the market is segmented into Patient Recruitment & Retention, Trial Design & Protocol Optimization, Data Management & Analytics, Monitoring & Safety Surveillance and Drug Discovery Support. Based on end user, the market is segmented into Pharmaceutical Companies, Biotechnology Companies, Contract Research Organizations (CROs), Academic & Research Institutes and Hospitals & Clinical Centers. Based on region, the market is segmented into North America, Europe, Asia Pacific, Latin America and Middle East & Africa.

Driver of the Global Ai In Clinical Trials Market

The global market for AI in clinical trials is significantly driven by the swift adoption of AI technologies, which optimize participant matching and enhance trial protocols, site selection, and recruitment processes. This integration minimizes delays and boosts the feasibility of studies. AI's capability to accurately identify eligible patients from electronic health records and real-world data enhances enrollment efficiency and ensures adherence to protocols. Moreover, advanced predictive modeling fosters improved resource allocation and risk management, motivating sponsors to embrace AI solutions. These operational efficiencies, along with perceived enhancements in trial quality, promote wider acceptance and seamless incorporation of AI into clinical development workflows.

Restraints in the Global Ai In Clinical Trials Market

The Global AI in Clinical Trials market faces significant challenges due to stringent regulations surrounding patient privacy and escalating concerns over data security. These issues restrict access to essential clinical datasets needed for developing effective AI models. The complexities associated with de-identifying nuanced clinical data and ensuring compliance across different regions complicate centralized data access and inhibit collaboration between institutions. This creates additional hurdles for vendors, ultimately limiting the diversity of data available for algorithm training, which can affect the reliability and applicability of AI models. Consequently, organizations may opt to postpone or limit the implementation of AI in clinical trials until adequate privacy protections and governance strategies are put in place.

Market Trends of the Global Ai In Clinical Trials Market

The Global AI in Clinical Trials market is witnessing a significant trend towards the integration of Real World Evidence (RWE) within its frameworks. AI platforms are adeptly processing diverse clinical and real-world data sources, enhancing the richness of evidence utilized for trial design, patient selection, and outcome assessment. This advancement fosters a closer alignment between trial results and routine clinical practices, as AI enables pattern recognition across varying care settings and unstructured data. As sponsors and investigators increasingly emphasize the need for interoperable models and explainable outputs, the translation of observational insights into actionable trial hypotheses is becoming more prevalent, effectively bridging evidence silos and boosting the relevance and utility of trial findings in everyday healthcare.

Table of Contents

Introduction

• Objectives of the Study
• Market Definition & Scope

Research Methodology

• Research Process
• Secondary & Primary Data Methods
• Market Size Estimation Methods

Executive Summary

• Global Market Outlook
• Key Market Highlights
• Segmental Overview
• Competition Overview

Market Dynamics & Outlook

• Macro-Economic Indicators
• Drivers & Opportunities
• Restraints & Challenges
• Supply Side Trends
• Demand Side Trends
• Porters Analysis & Impact
  • Competitive Rivalry
  • Threat of Substitute
  • Bargaining Power of Buyers
  • Threat of New Entrants
  • Bargaining Power of Suppliers

Key Market Insights

• Key Success Factors
• Market Impacting Factors
• Top Investment Pockets
• Ecosystem Mapping
• Market Attractiveness Index 2025
• PESTEL Analysis
• Regulatory Landscape

Global AI in Clinical Trials Market Size by Offering & CAGR (2026-2033)

• Market Overview
• Software
• Services
• Hardware

Global AI in Clinical Trials Market Size by AI Technology Type & CAGR (2026-2033)

• Market Overview
• Machine Learning
  • Supervised Learning
  • Unsupervised Learning
• Deep Learning
• Natural Language Processing (NLP)
• Computer Vision

Global AI in Clinical Trials Market Size by Clinical Trial Phase & CAGR (2026-2033)

• Market Overview
• Phase I
• Phase II
• Phase III
• Phase IV

Global AI in Clinical Trials Market Size by Therapeutic Area & CAGR (2026-2033)

• Market Overview
• Oncology
• Infectious Diseases
• Neurology
• Cardiovascular
• Metabolic Disorders
• Immunology
• Others

Global AI in Clinical Trials Market Size by Application & CAGR (2026-2033)

• Market Overview
• Patient Recruitment & Retention
• Trial Design & Protocol Optimization
• Data Management & Analytics
• Monitoring & Safety Surveillance
• Drug Discovery Support

Global AI in Clinical Trials Market Size by End User & CAGR (2026-2033)

• Market Overview
• Pharmaceutical Companies
• Biotechnology Companies
• Contract Research Organizations (CROs)
• Academic & Research Institutes
• Hospitals & Clinical Centers

Global AI in Clinical Trials Market Size & CAGR (2026-2033)

• North America (Offering, AI Technology Type, Clinical Trial Phase, Therapeutic Area, Application, End User)
  • US
  • Canada
• Europe (Offering, AI Technology Type, Clinical Trial Phase, Therapeutic Area, Application, End User)
  • Germany
  • Spain
  • France
  • UK
  • Italy
  • Rest of Europe
• Asia Pacific (Offering, AI Technology Type, Clinical Trial Phase, Therapeutic Area, Application, End User)
  • China
  • India
  • Japan
  • South Korea
  • Rest of Asia-Pacific
• Latin America (Offering, AI Technology Type, Clinical Trial Phase, Therapeutic Area, Application, End User)
  • Mexico
  • Brazil
  • Rest of Latin America
• Middle East & Africa (Offering, AI Technology Type, Clinical Trial Phase, Therapeutic Area, Application, End User)
  • GCC Countries
  • South Africa
  • Rest of Middle East & Africa

Competitive Intelligence

• Top 5 Player Comparison
• Market Positioning of Key Players, 2025
• Strategies Adopted by Key Market Players
• Recent Developments in the Market
• Company Market Share Analysis, 2025
• Company Profiles of All Key Players
  • Company Details
  • Product Portfolio Analysis
  • Company's Segmental Share Analysis
  • Revenue Y-O-Y Comparison (2023-2025)

Key Company Profiles

• Medidata (Dassault Systmes)
  • Company Overview
  • Business Segment Overview
  • Financial Updates
  • Key Developments
• IQVIA
  • Company Overview
  • Business Segment Overview
  • Financial Updates
  • Key Developments
• Unlearn.ai
  • Company Overview
  • Business Segment Overview
  • Financial Updates
  • Key Developments
• Owkin
  • Company Overview
  • Business Segment Overview
  • Financial Updates
  • Key Developments
• Saama Technologies
  • Company Overview
  • Business Segment Overview
  • Financial Updates
  • Key Developments
• AiCure
  • Company Overview
  • Business Segment Overview
  • Financial Updates
  • Key Developments
• Deep6.ai
  • Company Overview
  • Business Segment Overview
  • Financial Updates
  • Key Developments
• Exscientia
  • Company Overview
  • Business Segment Overview
  • Financial Updates
  • Key Developments
• Antidote Technologies
  • Company Overview
  • Business Segment Overview
  • Financial Updates
  • Key Developments
• BioSymetrics
  • Company Overview
  • Business Segment Overview
  • Financial Updates
  • Key Developments
• Euretos
  • Company Overview
  • Business Segment Overview
  • Financial Updates
  • Key Developments
• Ardigen
  • Company Overview
  • Business Segment Overview
  • Financial Updates
  • Key Developments
• Mendel AI
  • Company Overview
  • Business Segment Overview
  • Financial Updates
  • Key Developments
• Phesi
  • Company Overview
  • Business Segment Overview
  • Financial Updates
  • Key Developments
• ICON plc
  • Company Overview
  • Business Segment Overview
  • Financial Updates
  • Key Developments
• Parexel
  • Company Overview
  • Business Segment Overview
  • Financial Updates
  • Key Developments
• Syneos Health
  • Company Overview
  • Business Segment Overview
  • Financial Updates
  • Key Developments
• nference
  • Company Overview
  • Business Segment Overview
  • Financial Updates
  • Key Developments
• ConcertAI
  • Company Overview
  • Business Segment Overview
  • Financial Updates
  • Key Developments
• SAS Institute
  • Company Overview
  • Business Segment Overview
  • Financial Updates
  • Key Developments

Conclusion & Recommendations