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市場調查報告書
商品編碼
1513545

由人工智慧驅動的創新臨床試驗公司：策略分析與成長機會

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個工作天內

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簡介目錄

將現實世界的見解融入臨床試驗管理可快速人工智慧在臨床試驗中的採用

隨著複雜的新療法在世界各地的臨床管道中激增，透過適應性試驗設計以及技術支援的規劃和執行解決方案來改進試驗設計的趨勢日益明顯。人工智慧 (AI) 在支援分散式試驗設計和實現以患者為中心的臨床試驗方法方面獲得了廣泛認可。臨床試驗依賴電子健康記錄(EMR) 形式的大型縱向病患資料庫。儘管有強大的資料庫，但大多數資料庫缺乏清晰度和結構，導致難以閱讀。因此，人工智慧/機器學習 (ML) 演算法和平台的快速普及使得建立非結構化資料庫變得更加容易，而電子健康記錄(EHR) 的使用正在改變世界各地臨床試驗的格局。且相關的資料來源，具有巨大的改進潛力。

將人工智慧主導的整合解決方案納入臨床試驗設計、地點選擇、患者識別和保留，將簡化各種 CRO 和製藥公司的打入市場策略。人工智慧在臨床試驗中變得越來越重要，可以降低成本、提高效率，並透過遠端患者招募、管理和參與支援向分散式試驗的過渡。語音辨識、聊天機器人和其他設備形式的互動平台可以提高患者的依從性並留住更多患者。這些平台對於選擇適當的臨床實驗和臨床實驗地點也非常有價值。 RCT 是人工智慧應用不斷擴展的另一個重要領域，申辦者可以利用這項技術來分析產生的大量站點級資料集，並了解試驗設計和實施情況。

Icon plc、Novotech、Syneos Health 和 IQVIA 等領先的 CRO 以及 BMS 等多家製藥公司已部署基於人工智慧的平台來支援設施選擇和患者招募。 BMS、安進、阿斯特捷利康、諾華等許多其他公司也在臨床試驗中應用人工智慧，以實現各個階段的最佳化，以縮短整體臨床試驗時間。

人工智慧為臨床試驗帶來了根本性創新，包括收集和分析 RWD、無縫結合 I 期和 II 期臨床試驗以及開發以患者為中心的新型終點。還可以利用人工智慧從各種輸入創建標準化、結構化的數位資料元素。人工智慧驅動的試驗設計最佳化並加速以患者為中心的設計創建，顯著減輕患者負擔，增加成功的可能性，減少修改，並提高試驗的整體效率。領先的技術供應商和製藥Start-Ups之間的合作正在為未來更有效的臨床試驗奠定基礎。

目錄

策略要務

  • 為什麼成長如此困難？
  • The Strategic Imperative 8（TM）
  • 關鍵策略要務對人工智慧驅動的臨床試驗產業的影響
  • 成長機會推動Growth Pipeline Engine（TM）

生態系統

  • 分析範圍
  • 分割
  • 藥物開發供應商生態系統
  • 人工智慧供應商生態系統
  • 在臨床試驗中使用人工智慧的價值提案
  • 基於獨特提案主張的策略概況

成長機會分析

  • 生長促進因子
  • 成長抑制因素
  • 監管場景：人工智慧在臨床試驗中的應用
  • ConcertAI：公司概況
  • ConcertAI：價值提案
  • ConcertAI：成長策略
  • 忘卻：公司概況
  • 忘記：價值提案
  • 忘記：成長策略
  • Phesi：公司概況
  • Phesi：價值提案
  • Phesi：成長策略
  • QuantHealth：公司簡介
  • QuantHealth：價值提案
  • QuantHealth：成長策略
  • 歐金：公司概況
  • 歐金：價值提案
  • 歐金：成長策略
  • Deep 6 AI：公司概況
  • Deep 6 AI：價值提案
  • Deep 6 AI：成長策略
  • 範式：公司概述
  • 範式：價值提案
  • 範式：成長策略
  • 孟德爾健康：公司簡介
  • 孟德爾健康：價值提案
  • 孟德爾健康：成長策略
  • Oncoshot：公司概況
  • Oncoshot：價值提案
  • Oncoshot：成長策略
  • Amazon Web Services, Inc.
  • AWS：價值提案
  • AWS：成長策略

成長機會宇宙

  • 成長機會 1：與聯合資料系統的資料互通性
  • 成長機會 2：利用法學碩士進行資料重組和分發，以進行病患識別和登記
  • 成長機會3：基於RWD/RWE的腫瘤試驗設計與通訊協定最佳化
  • 材料清單
  • 免責聲明
簡介目錄
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
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