Growth Opportunities and Innovative Use Cases for AI in Clinical Trials
Description
Growth Opportunities and Innovative Use Cases for AI in Clinical Trials
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?
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
64 Pages
- 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™
- 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
- 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
- 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
- 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
- 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
- Growth Opportunity 1-Remote Recruitment to Expand Patient Diversity for Cancer Trials
- Growth Opportunity 2-Patient-centric Clinical Trial Design for Better Retention and Monitoring
- Growth Opportunity 3-AI-integrated Cloud-based SaaS Delivery Models
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