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市场调查报告书
商品编码
1513545

由人工智慧驱动的创新临床试验公司：策略分析与成长机会

Innovative AI-enabled Clinical Trial Companies: Strategic Profiling and Growth Opportunities
出版日期: | 出版商: Frost & Sullivan | 英文 60 Pages | 商品交期: 最快1-2个工作天内

价格
简介目录

将现实世界的见解融入临床试验管理可快速人工智慧在临床试验中的采用

随着复杂的新疗法在世界各地的临床管道中激增，透过适应性试验设计以及技术支援的规划和执行解决方案来改进试验设计的趋势日益明显。人工智慧 (AI) 在支援分散式试验设计和实现以患者为中心的临床试验方法方面获得了广泛认可。临床试验依赖电子健康记录(EMR) 形式的大型纵向病患资料库。儘管有强大的资料库，但大多数资料库缺乏清晰度和结构，导致难以阅读。因此，人工智慧/机器学习 (ML) 演算法和平台的快速普及使得建立非结构化资料库变得更加容易，而电子健康记录(EHR) 的使用正在改变世界各地临床试验的格局。且相关的资料来源，具有巨大的改进潜力。

将人工智慧主导的整合解决方案纳入临床试验设计、地点选择、患者识别和保留，将简化各种 CRO 和製药公司的打入市场策略。人工智慧在临床试验中变得越来越重要，可以降低成本、提高效率，并透过远端患者招募、管理和参与支援向分散式试验的过渡。语音辨识、聊天机器人和其他设备形式的互动平台可以提高患者的依从性并留住更多患者。这些平台对于选择适当的临床实验和临床实验地点也非常有价值。 RCT 是人工智慧应用不断扩展的另一个重要领域，申办者可以利用这项技术来分析产生的大量站点级资料集，并了解试验设计和实施情况。

Icon plc、Novotech、Syneos Health 和 IQVIA 等领先的 CRO 以及 BMS 等多家製药公司已部署基于人工智慧的平台来支援设施选择和患者招募。 BMS、安进、阿斯特捷利康、诺华等许多其他公司也在临床试验中应用人工智慧，以实现各个阶段的最佳化，以缩短整体临床试验时间。

人工智慧为临床试验带来了根本性创新，包括收集和分析 RWD、无缝结合 I 期和 II 期临床试验以及开发以患者为中心的新型终点。还可以利用人工智慧从各种输入创建标准化、结构化的数位资料元素。人工智慧驱动的试验设计优化并加速以患者为中心的设计创建，显着减轻患者负担，增加成功的可能性，减少修改，并提高试验的整体效率。领先的技术供应商和製药Start-Ups之间的合作正在为未来更有效的临床试验奠定基础。

目录

策略要务

  • 为什么成长如此困难？
  • The Strategic Imperative 8（TM）
  • 关键策略要务对人工智慧驱动的临床试验产业的影响
  • 成长机会推动Growth Pipeline Engine（TM）

生态系统

  • 分析范围
  • 分割
  • 药物开发供应商生态系统
  • 人工智慧供应商生态系统
  • 在临床试验中使用人工智慧的价值提案
  • 基于独特提案主张的策略概况

成长机会分析

  • 生长促进因子
  • 成长抑制因素
  • 监管场景：人工智慧在临床试验中的应用
  • ConcertAI：公司概况
  • ConcertAI：价值提案
  • ConcertAI：成长策略
  • 忘却：公司概况
  • 忘记：价值提案
  • 忘记：成长策略
  • Phesi：公司概况
  • Phesi：价值提案
  • Phesi：成长策略
  • QuantHealth：公司简介
  • QuantHealth：价值提案
  • QuantHealth：成长策略
  • 欧金：公司概况
  • 欧金：价值提案
  • 欧金：成长策略
  • Deep 6 AI：公司概况
  • Deep 6 AI：价值提案
  • Deep 6 AI：成长策略
  • 范式：公司概述
  • 范式：价值提案
  • 范式：成长策略
  • 孟德尔健康：公司简介
  • 孟德尔健康：价值提案
  • 孟德尔健康：成长策略
  • Oncoshot：公司概况
  • Oncoshot：价值提案
  • Oncoshot：成长策略
  • Amazon Web Services, Inc.
  • AWS：价值提案
  • AWS：成长策略

成长机会宇宙

  • 成长机会 1：与联合资料系统的资料互通性
  • 成长机会 2：利用法学硕士进行资料重组和分发，以进行病患识别和登记
  • 成长机会3：基于RWD/RWE的肿瘤试验设计与通讯协定优化
  • 材料清单
  • 免责声明
简介目录
Product Code: PFKD-52

The Integration of Real-world Insights into Trial Management is Propelling AI Adoption in Clinical Trials

As global clinical pipelines witness a surge in complex novel therapies, there is a general inclination toward improving trial design through adaptive trial designs with technology-enabled solutions for planning and execution. Artificial intelligence (AI) is gaining large-scale recognition in terms of supporting decentralized trial designs and allowing patient-centric clinical trial modalities. Clinical trials rely on large-scale longitudinal patient databases in the form of electronic medical records (EMRs). Despite the availability of robust databases, most lack clarity and structure, making them difficult to read. As a result, the rapid adoption of AI/machine learning (ML) algorithms and platforms allows easy structuring of unstructured databases, and the use of electronic health records (EHRs) represents a vast, rich, and highly relevant data source that holds tremendous potential to improve the global clinical trial landscape.

Incorporating integrated AI-driven solutions in clinical trial design, site selection, and patient identification and retention will ease the go-to-market strategy for various CROs and pharmaceutical companies. AI is gaining significance in clinical trials to reduce cost, increase efficiency, and support the transition to decentralized trials through remote patient recruitment, management, and engagement. Interactive platforms in the form of voice recognition, chatbots, and other devices ensure better patient adherence and greater retention. These platforms are also highly beneficial in the selection of appropriate investigators and trial sites. Randomized control trials (RCTs) represent another important area seeing increased AI application, where sponsors can leverage the technology to analyze the vast site-level datasets generated for greater visibility into trial design and implementation.

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

AI brings innovation fundamental to transform clinical trials, such as collecting and analyzing RWD, seamlessly combining phase I and II of clinical trials, and developing novel patient-centric endpoints. AI can also be leveraged to create standardized, structured, and digital data elements from a range of inputs. 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, large technology providers and pharmaceutical start-ups are setting the stage for more effective clinical trials in the future.

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 the AI-enabled Clinical Trials Industry
  • Growth Opportunities Fuel the Growth Pipeline Engine™

Ecosystem

  • Scope of Analysis
  • Segmentation
  • Drug Development Vendor Ecosystem
  • AI Vendor Ecosystem
  • Value Proposition of Using AI in Clinical Trials
  • Strategic Profiles Based on Unique Value Proposition

Growth Opportunity Analysis

  • Growth Drivers
  • Growth Restraints
  • Regulatory Scenario: AI Use in Clinical Trials
  • ConcertAI: Company Overview
  • ConcertAI: Value Proposition
  • ConcertAI: Growth Strategy
  • Unlearn: Company Overview
  • Unlearn: Value Proposition
  • Unlearn: Growth Strategy
  • Phesi: Company Overview
  • Phesi: Value Proposition
  • Phesi: Growth Strategy
  • QuantHealth: Company Overview
  • QuantHealth: Value Proposition
  • QuantHealth: Growth Strategy
  • Owkin: Company Overview
  • Owkin: Value Proposition
  • Owkin: Growth Strategy
  • Deep 6 AI: Company Overview
  • Deep 6 AI: Value Proposition
  • Deep 6 AI: Growth Strategy
  • Paradigm: Company Overview
  • Paradigm: Value Proposition
  • Paradigm: Growth Strategy
  • Mendel Health: Company Overview
  • Mendel Health: Value Proposition
  • Mendel Health: Growth Strategy
  • Oncoshot: Company Overview
  • Oncoshot: Value Proposition
  • Oncoshot: Growth Strategy
  • Amazon Web Services, Inc.
  • AWS: Value Proposition
  • AWS: Growth Strategy

Growth Opportunity Universe

  • Growth Opportunity 1: Data Interoperability with Federated Data Systems
  • Growth Opportunity 2: Data Restructuring and Distribution with LLMs for Patient Identification and Enrollment
  • Growth Opportunity 3: RWD/RWE-based Oncology Trial Design and Protocol Optimization
  • List of Exhibits
  • Legal Disclaimer