Artificial intelligence is transforming the decentralized trials market by enabling more efficient, patient-centric, and data-driven clinical trial execution. AI-powered platforms improve patient enrollment, optimize protocol design, support real-time remote monitoring, and enhance data quality and regulatory compliance. By enabling faster analysis of large volumes of clinical data, AI helps identify safety signals earlier, reduce protocol deviations, accelerate enrollment, improve patient retention, and strengthen trial efficiency across areas such as oncology, cardiovascular diseases, and rare disease research.
Report Includes
- Impact analysis of artificial intelligence on the global decentralized clinical trials (DCT) market
- Case studies for successful integration of AI and machine learning (ML) algorithms in the design and efficiency of clinical trials
- Insights of current and evolving trends of AI adoption across the major geographic regions, including North America, Europe and the Asia-Pacific
- Review of AI’s role in clinical development featuring improved efficiency and patient recruitment, faster data analysis, and costs reduction
- Understanding of the market disruptions across key industry verticals and AI impact on various stages of value chain
- Analysis of the companies’ market spendings and forecasting analysis, and a VC investment outlook
Table of Contents
Chapter 1 Key Use Cases for AI Adoption in Decentralized Clinical Trials- Overview
- Key Use Cases
- Case 1: AI-Driven Patient Pre-Screening and Recruitment
- Case 2: Intelligent Risk-Based Monitoring and Data Quality Assurance
- Case 3: Predictive Patient Engagement and Retention
- Case 4: Computer Vision for Remote Protocol Adherence and Endpoint Capture
- Adoption Trends at the Geography Level
- Adoption Trends in North America
- Adoption Trends in Europe
- Adoption Trends in Asia-Pacific
- Adoption Trends in the Rest of the World
- Impact of AI on Key Industry Verticals
- AI-Enabled Trial Design, Feasibility, and Protocol Optimization
- AI-Driven Patient Identification, Recruitment, and Diversity Enablement
- Intelligent Remote Patient Monitoring and Safety Management
- Data Integrity, Quality Assurance, and Regulatory Readiness
- AI-Enabled Adaptive and Hybrid Trial Execution
- Impact of AI on Various Stages of the Supply Chain
- Key Spending by Companies on AI
- Abbreviations Used in the Report
Table 1: Key Spending by Companies on AI
Table 2: Abbreviations Used in This Report
