人工智能在临床试验中的增长机会和创新用例

AI 技术可用于转化临床试验，例如收集和分析真实世界的数据，无缝结合 I 期和 II 期临床试验，以及开发新的以患者为中心的端点。这是一项基础性创新。 利用人工智能从各种输入中创建标准化、结构化和数字化的数据元素，人工智能驱动的研究设计优化并加速了以患者为中心的设计的创建。，可以显着减轻患者负担，增加成功的可能性，减少数量修订，提高整体审理效率。 大型技术提供商和製药初创公司都为未来更有效的临床试验指明了方向。

本报告考察了临床试验市场中的 AI，并提供了市场概况、战略要务、增长机会等。

内容

战略要务

• 为什么增长越来越难？
• 战略要务
• 3 大战略要务对临床试验中 AI 的影响
• 增长机会加速增长管道引擎

增长机会分析

• 分析范围
• 定义
• 细分
• 临床试验的三大问题
• AI 在临床试验中的价值主张
• 为什么 AI 对临床试验的成功至关重要
• 通过 AI 支持的临床试验了解患者趋势
• 增长动力
• 抑制增长的因素
• 监管场景 - 在临床试验中使用 AI
• 供应商生态系统
• 积极参与临床试验的公司
• 在临床试验中采用 AI 的时间表和影响

用例 - 临床试验设计

用例 - 患者充实、招募、註册

用例 - 患者监测、医疗依从性和保留

用例 - 调查员和地点选择

其他值得关注的公司

• 其他值得关注的公司

成长机会

• 增长机会 1 - 远程招募以扩大癌症试验中患者的多样性
• Growth Opportunity2 以患者为中心的临床试验设计可实现更好的保留和监测
• 增长机会 3 - 具有集成 AI 的基于云的 SaaS 交付模型
• 附件列表
• 免责声明

Integrating Real-world Insights into Intelligent Platforms to Enable Patient-centric Trial Design

As clinical pipelines globally witness a surge in novel complex therapies, the clinical trial industry demands new tools in predictive analytics to improve trial design, planning, and execution. Artificial intelligence is gaining large-scale recognition as support for decentralized trial designs, thus enabling patient-centric clinical trial designs. The rapid adoption of AI/ML algorithms and platforms to structure and utilize electronic health records (EHRs) allows the industry to tap into a vast, rich, and highly relevant data source that holds tremendous potential in improving the global clinical trial landscape.

Incorporating integrated AI-driven solutions in clinical trial design and patient retention will ease the go-to-market strategy for various CROs and pharma players as they will reduce costs, increase efficiency, and support the transition to decentralized trials by means of remote patient recruitment, management, as well as engagement through interactive platforms thus ensuring higher retention. Additionally, these platforms are highly beneficial in the selection of appropriate investigators and trial sites. Randomized control trials (RCTs) are another possible application for sponsors to leverage AI in analyzing vast site-level datasets for greater insight into trial design and implementation.

Leading CROs such as Syneos Health or IQVIA, as well as several pharmaceutical companies such as BMS, have successfully deployed AI-based platforms to support site selection and patient recruitment. Companies (including AstraZeneca and Novartis among others) are also applying AI in clinical trials to enable the optimization of different stages with the intent of reducing the overall trial timelines.

AI technologies bring fundamental innovations for transforming clinical trials, such as collecting and analyzing real-world data, seamlessly combining phases I and II of clinical trials, and developing novel patient-centered endpoints. AI can be leveraged to create standardized, structured, and digital data elements from a range of inputs, and as AI-enabled study design helps optimize and accelerate the creation of patient-centric designs, it significantly reduces patient burden, increases the likelihood of success, decreases the number of amendments, and improves the overall efficiency of trials. Together, big technology providers and pharmaceutical start-ups are setting the course for more effective clinical trials in the future.

Key Issues Addressed:

• What are the key trends impacting the clinical trial industry in terms of technology implementation?

• What are the various application areas for AI in terms of execution of clinical trials?

• Who are some of the key industry stakeholders building cutting-edge AI enabled platforms?

• What are the industry drivers and barriers impacting the AI enabled clinical trial industry?

• What are the key strategies global stakeholders are taking to better serve customers while ensuring growth?

• What are the key growth opportunities going forward and call to action for CROs, sponsors and technology participants in the ecosystem?

Table of Contents

Strategic Imperatives

• Why Is It Increasingly Difficult to Grow?
• The Strategic Imperative 8™
• The Impact of the Top 3 Strategic Imperatives on Artificial Intelligence (AI) in the Clinical Trials Industry
• Growth Opportunities Fuel the Growth Pipeline Engine™

Growth Opportunity Analysis

• Scope of Analysis
• Definitions
• Segmentation
• The Top 3 Clinical Trial Challenges
• The AI Value Proposition in Clinical Trials
• Why AI Is Critical for Trial Success
• The Patient Journey Through AI-enabled Clinical Trials
• Growth Drivers
• Growth Restraints
• Regulatory Scenario-AI Use in Clinical Trials
• Vendor Ecosystem
• AI in Clinical Trials-Companies-to-Action (C2A) Targets
• AI in Clinical Trials-Adoption Timeline and Impact

Use Case-Clinical Trial Design

• AI Applications in Clinical Trial Design
• Vendor Spotlight-Owkin
• Industry Use Case and Analyst Perspective
• Vendor Spotlight-ConcertAI
• Industry Use Case and Analyst Perspective
• Other AI Vendors in Clinical Trial Design

Use Case-Patient Enrichment, Recruitment, and Enrollment

• AI Application in Patient Enrichment, Recruitment, and Enrollment
• Vendor Spotlight-Unlearn
• Industry Use Case and Analyst Perspective
• Vendor Spotlight-TrialWire
• Analyst Perspective
• Other AI Vendors for Patient Enrichment, Recruitment, and Enrollment

Use Case-Patient Monitoring, Medical Adherence, and Retention

• AI Application in Patient Monitoring, Adherence, and Retention
• Vendor Spotlight-AiCure
• Industry Use Case and Analyst Perspective
• Vendor Spotlight-AWS
• Industry Use Case and Analyst Perspective
• Other AI Vendors for Patient Monitoring, Adherence, and Retention

Use Case-Investigator and Site Selection

• AI Applications in Investigator and Site Selection
• Vendor Spotlight-Medidata AcornAI
• Industry Use Case and Analyst Perspective
• Vendor Spotlight-Deep 6 AI
• Industry Use Case and Analyst Perspective
• Other AI Vendors for Investigator and Site Selection

Other Companies to Watch

• Other Companies to Watch
• Other Companies to Watch (continued)

Growth Opportunity Universe

• Growth Opportunity 1-Remote Recruitment to Expand Patient Diversity for Cancer Trials
• Growth Opportunity 1-Remote Recruitment to Expand Patient Diversity for Cancer Trials (continued)
• Growth Opportunity 2-Patient-centric Clinical Trial Design for Better Retention and Monitoring
• Growth Opportunity 2-Patient-centric Clinical Trial Design for Better Retention and Monitoring (continued)
• Growth Opportunity 3-AI-integrated Cloud-based SaaS Delivery Models
• Growth Opportunity 3-AI-integrated Cloud-based SaaS Delivery Models (continued)
• List of Exhibits
• Legal Disclaimer