Innovative AI-enabled Clinical Trial Companies: Strategic Profiling and Growth Opportunities

Innovative AI-enabled Clinical Trial Companies: Strategic Profiling and Growth Opportunities

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

RELEASE DATE
12-Jun-2024
REGION
Global
Deliverable Type
Market Research
Research Code: PFKD-01-00-00-00
SKU: HC_2024_794
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Description

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.

Author: Aarti Siddhesh Chitale

Table of Contents

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™

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 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 1: Data Interoperability with Federated Data Systems

Growth Opportunity 1: Data Interoperability with Federated Data Systems (continued)

Growth Opportunity 2: Data Restructuring and Distribution with LLMs for Patient Identification and Enrollment

Growth Opportunity 2: Data Restructuring and Distribution with LLMs for Patient Identification and Enrollment (continued)

Growth Opportunity 3: RWD/RWE-based Oncology Trial Design and Protocol Optimization

Growth Opportunity 3: RWD/RWE-based Oncology Trial Design and Protocol Optimization (continued)

List of Exhibits

Legal Disclaimer

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. Author: Aarti Siddhesh Chitale
More Information
Deliverable Type Market Research
Author Aarti Siddhesh Chitale
Industries Healthcare
No Index No
Is Prebook No
Podcast No
Predecessor PDA0-01-00-00-00
WIP Number PFKD-01-00-00-00