人工智能在臨床試驗中的增長機會和創新用例

AI 技術可用於轉化臨床試驗，例如收集和分析真實世界的數據，無縫結合 I 期和 II 期臨床試驗，以及開發新的以患者為中心的端點。這是一項基礎性創新。 利用人工智能從各種輸入中創建標準化、結構化和數字化的數據元素，人工智能驅動的研究設計優化並加速了以患者為中心的設計的創建。，可以顯著減輕患者負擔，增加成功的可能性，減少數量修訂，提高整體審理效率。 大型技術提供商和製藥初創公司都為未來更有效的臨床試驗指明了方向。

本報告考察了臨床試驗市場中的 AI，並提供了市場概況、戰略要務、增長機會等。

內容

戰略要務

• 為什麼增長越來越難？
• 戰略要務
• 3 大戰略要務對臨床試驗中 AI 的影響
• 增長機會加速增長管道引擎

增長機會分析

• 分析範圍
• 定義
• 細分
• 臨床試驗的三大問題
• AI 在臨床試驗中的價值主張
• 為什麼 AI 對臨床試驗的成功至關重要
• 通過 AI 支持的臨床試驗了解患者趨勢
• 增長動力
• 抑制增長的因素
• 監管場景 - 在臨床試驗中使用 AI
• 供應商生態系統
• 積極參與臨床試驗的公司
• 在臨床試驗中採用 AI 的時間表和影響

用例 - 臨床試驗設計

用例 - 患者充實、招募、註冊

用例 - 患者監測、醫療依從性和保留

用例 - 調查員和地點選擇

其他值得關注的公司

• 其他值得關注的公司

成長機會

• 增長機會 1 - 遠程招募以擴大癌症試驗中患者的多樣性
• Growth Opportunity2 以患者為中心的臨床試驗設計可實現更好的保留和監測
• 增長機會 3 - 具有集成 AI 的基於雲的 SaaS 交付模型
• 附件列表
• 免責聲明

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