AI를 활용한 혁신적인 임상시험 기업 : 전략적 프로파일링과 성장 기회

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