AI-based Clinical Trial Solution Providers Market Landscape 2020 to 2030 - Who are the Leading AI-based Clinical Trial Solution Providers

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Aug 21, 2020, 10:00 ET

DUBLIN, Aug. 21, 2020 /PRNewswire/ -- The "AI-based Clinical Trial Solution Providers Market, 2020-2030" report has been added to ResearchAndMarkets.com's offering.

This report features an extensive study of the companies offering AI-based platforms for clinical trial applications, in addition to the current market landscape and their future potential.

One of the key objectives of the report was to understand the primary growth drivers and estimate the future opportunity within this market. Based on several parameters, such as annual number of clinical trials, average capital investment per trial across different phases and therapeutic areas, cost saving potential of AI and expected annual growth rate across various geographies, we have provided an informed estimate of the likely evolution of the market, in the mid to long term, for the period 2020-2030.

The chapter features the likely distribution of the opportunity across different:

[A] trial phase (phase I, phase II and phase III)
[B] therapeutic areas (cardiovascular disorders, CNS disorders, infectious disorders, metabolic disorders, oncological disorders and other disorders)
[C] end-users (pharmaceutical companies, and academia and other users)
[D] key geographical regions (North America, Europe, Asia-Pacific and Rest of the world)

Market Insights

The process of successfully developing a novel therapeutic intervention is both time and cost-intensive. In fact, it is estimated that a prescription drug requires around 10 years and over USD 2.5 billion in capital investment, before reaching the market. In this process, clinical trials are a crucial requirement, enabling both innovators and regulators to assess the efficacy of a candidate drug and establish whether it is safe for use in humans.

It is estimated that nearly 50% of the total time and capital expenditure during the drug development process, is on conducting clinical research. However, all trials are not successful; they are prone to delays (due to various reasons), and failure, both of which are known to impose enormous financial burdens on sponsors.

According to a study conducted by the MIT Sloan School of Management, the rate of clinical success, defined as the proportion of trials that result in approval of the drug/therapy under investigation, was currently estimated to be 14%. The study further demonstrated that there is significant variance in the aforementioned rate across different types of therapies; for instance, for vaccines against infectious diseases, clinical success was estimated to be slightly above 30%, while for investigational anti-cancer drugs, it was 3%.

Some of the key factors responsible for clinical-stage product failure include inadequate study design, insufficient/incomplete patient recruitment, improper subject stratification during study conduct, and high rate of participant attrition.

In attempts to address the abovementioned challenges, stakeholders in the pharmaceutical industry are actively exploring diverse strategies and solutions, one of which involves the collection and processing of real-world data. In fact, real-world data analysis is deemed to possess the potential to offer valuable insights from patient/healthcare provider testimonies, in order to drive future trial optimization efforts and facilitate better decision making during clinical research conduct.

However, in order to generate actionable insights from real-world medical data, there is a need for robust and advanced data mining technologies, such as big data analytics and artificial intelligence (AI) powered tools.

Data integration, evolutionary modelling and pattern recognition using predictive AI models, can enable trial sponsors to aggregate, curate, and analyze large volumes of data, thereby, harnessing information captured during past trials to drive future therapy development initiatives. Experts also believe that the use of AI-powered solutions have the potential to address some of the commonly reported challenges, such as concerns related to clinical trial design, patient recruitment and retention, site selection, medical data interpretation and evaluation of treatment efficacy, which are encountered during trial conduct.

Considering that the aforementioned issues are addressed, it is safe to presume that opting to use AI-enabled technologies in clinical trials may eventually improve clinical R&D, and allow innovators to optimize on both time and capital investments made in such initiatives.

Currently, this technology is still in its early stages, with limited adoption across the world. However, it is worth mentioning that close to USD 4 billion was invested into AI-focused healthcare startups, in 2019. We are led to believe that the opportunity for AI-based solution providers within the healthcare industry is likely to grow at a significant in the foreseen future.

Amongst other elements, the report features:

A detailed assessment of the competitive landscape of AI-based solution providers based on parameters, such as area of application, year of establishment, company size and location of headquarters.
Brief profiles of prominent players engaged in offering AI-based solutions for clinical trial applications. Each profile features a brief overview of the company and its proprietary technology platform(s), recent developments, and an informed future outlook.
An analysis of the partnerships and collaborations inked in the domain, in the period between 2014 and 2020 (till May), based on several parameters, such as year of partnership, type of partnership, application mentioned in agreement, target therapeutic area mentioned in the agreement, year of partnership and type of partner, most active players and geographical analysis.
An analysis of the funding and investments made in the domain, in the period between 2014 and 2020 (till May), including seed financing, venture capital financing, debt financing, grants, capital raised from IPOs and subsequent offerings, at various stages of development in companies that are engaged in this field, based on several parameters, such as number of funding instances, amount invested, type of funding, leading players and investors, and geographical analysis
A detailed analysis of completed, ongoing and planned clinical trials involving the use of AI, based on multiple parameters, such as trial registration year, trial phase, trial status, type of sponsor/collaborator, target therapeutic area, trial design, top sponsor, geographical location of the trial and enrolled patient population.
An analysis of various AI-related initiatives of top 10 big pharma players (based on revenue), based on multiple parameters, such as year of initiative, type of initiative, focus of initiative, area of application and target therapeutic area. In addition, leading players and leading partners have been highlighted based on the number of initiatives.
A case study on recent use cases, wherein various pharmaceutical/healthcare companies have employed AI-based solutions for different processes of clinical trials, highlighting different business needs of such players and key takeaways of the solution provided by AI-based solution providers.
An in-depth analysis of the cost-saving potential across various processes of clinical drug development that can be brought about by the implementation of bespoke AI-based solutions.

In order to account for future uncertainties and to add robustness to our model, we have provided three forecast scenarios, portraying the conservative, base, and optimistic tracks of the market's evolution. The opinions and insights presented in this study were influenced by discussions conducted with multiple stakeholders in this domain.

Key Questions Answered

Who are the leading AI-based clinical trial solution providers?
How has the clinical activity involving the use of AI evolved in recent years?
What is the focus area of big pharma players in the AI domain?
Which companies have raised a significant amount of money in the domain?
What is the total cost-saving potential of AI-based clinical solutions across different steps of a clinical trial?
What kind of partnership models are presently being used by stakeholders in the industry?
What factors are likely to influence the evolution of this upcoming market?
How is the current and future opportunity likely to be distributed across key market segments?

Key Topics Covered

1. PREFACE
1.1. Scope of the Report
1.2. Research Methodology
1.3. Chapter Outlines

2. EXECUTIVE SUMMARY

3. INTRODUCTION
3.1. Chapter Overview
3.2. Overview of Artificial Intelligence (AI)
3.2.1. Machine Learning
3.2.2. Natural Language Processing
3.2.3. Classification of AI
3.2.3.1. Reactive AI
3.2.3.2. Limited Memory AI
3.2.3.3. Theory of Mind AI
3.2.3.4. Self-Aware AI
3.2.3.5. Artificial Narrow Intelligence
3.2.3.6. Artificial General Intelligence
3.2.3.7. Artificial Super Intelligence
3.2.4. Application of AI in Healthcare
3.2.4.1. Drug Discovery
3.2.4.2. Drug Manufacturing
3.2.4.3. Drug Marketing
3.2.4.4. Diagnosis and Treatment
3.2.4.5. Clinical Trials
3.2.4.5.1. Patient Recruitment
3.2.4.5.2. Patient Monitoring
3.2.4.5.3. Patient Adherence
3.3. Key Challenges Associated with the Adoption of AI
3.4. Future Perspectives

4. MARKET LANDSCAPE
4.1. Chapter Overview
4.2. AI-based Clinical Trial Solution Providers: Overall Market Landscape
4.2.1. Analysis by Area of Application
4.2.2. Analysis by Year of Establishment
4.2.3. Analysis by Company Size
4.2.4. Analysis by Location of Headquarters

5. COMPANY PROFILES
5.1. Chapter Overview
5.2. AiCure
5.2.1. Company and Technology Overview
5.2.2. Recent Developments and Future Outlook
5.3. Antidote
5.4. Deep Lens
5.5. Deep 6 AI
5.6. Innoplexus
5.7. Median Technologies
5.8. Mendel.ai
5.9. Phesi
5.10. Saama Technologies
5.11. Trials.ai

6. PARTNERSHIPS AND COLLABORATIONS
6.1. Chapter Overview
6.2. Partnership Models
6.3. AI-based Clinical Trial Solution Providers: Partnerships and Collaborations
6.3.1. Analysis by Year of Partnership
6.3.2. Analysis by Type of Partnership
6.3.3. Analysis by Application Mentioned in the Agreement
6.3.4. Analysis by Target Therapeutic Area Mentioned in the Agreement
6.3.5. Analysis by Year of Partnership and Type of Partner
6.3.6. Most Active Players: Analysis by Number of Partnerships
6.3.7. Geographical Analysis
6.3.8. Intercontinental and Intracontinental Agreements

7. FUNDING AND INVESTMENT ANALYSIS
7.1. Chapter Overview
7.2. Types of Funding Instances
7.3. AI-based Clinical Trial Solution Providers: Funding and Investments
7.3.1. Analysis by Number of Funding Instances
7.3.2. Analysis by Amount Invested
7.3.3. Analysis by Type of Funding
7.3.4. Leading Players: Analysis by Amount Invested and Number of Funding Instances
7.3.5. Most Active Investors: Analysis by Number of Funding Instances
7.3.6. Geographical Analysis by Amount Invested
7.4. Concluding Remarks

8. CLINICAL TRIAL ANALYSIS
8.1. Chapter Overview
8.2. Scope and Methodology
8.3. AI-based Clinical Trial Solution Providers: Analysis of Clinical Research Activity
8.3.1. Analysis by Trial Registration Year
8.3.2. Analysis by Trial Phase
8.3.3. Analysis by Trial Status
8.3.4. Analysis by Type of Sponsor/Collaborator
8.3.5. Analysis by Target Therapeutic Area
8.3.6. Analysis by Trial Design
8.3.7. Geographical Analysis by Number of Clinical Trials
8.3.8. Geographical Analysis by Enrolled Patient Population
8.3.9. Geographical Analysis by Number of Clinical Trials and Trial Status
8.3.10. Geographical Analysis by Enrolled Patient Population and Trial Status

9. BIG PHARMA INITIATIVES
9.1. Chapter Overview
9.1.1. Analysis by Year of Initiative
9.1.2. Analysis by Type of Initiative
9.1.3. Analysis by Focus of Initiative
9.1.4. Analysis by Area of Application
9.1.5. Analysis by Target Therapeutic Area

10. CASE STUDY: USE CASES
10.1. Chapter Overview
10.2. Roche and AiCure
10.2.1. Roche
10.2.2. AiCure
10.2.3. Business Needs
10.2.4. Objectives Achieved and Solutions Provided
10.3. Takeda and AiCure
10.3.1. Takeda
10.3.2. AiCure
10.3.3. Business Needs
10.3.4. Objectives Achieved and Solutions Provided
10.4. Teva Pharmaceuticals and Intel
10.4.1. Teva Pharmaceuticals
10.4.2. Intel
10.4.3. Business Needs
10.4.4. Objectives Achieved and Solutions Provided
10.5. Unnamed Pharmaceutical Company and Antidote
10.5.1. Antidote
10.5.2. Business Needs
10.5.3. Objectives Achieved and Solutions Provided
10.6. Unnamed Pharmaceutical Company and Cognizant
10.6.1. Cognizant
10.6.2. Business Needs
10.6.3. Objectives Achieved and Solutions Offered
10.7. Cedars-Sinai Medical Center and Deep 6 AI
10.7.1. Cedars-Sinai Medical Center
10.7.2. Deep 6 AI
10.7.3. Business Needs
10.7.4. Objectives Achieved and Solutions Offered

11. COST SAVING ANALYSIS
11.1. Chapter Overview
11.2. Key Assumptions and Methodology
11.3. Overall Cost Saving Potential of AI-based Clinical Trial Solutions, 2020-2030
11.3.1. Cost Saving Potential in Phase I Clinical Trials, 2020-2030
11.3.2. Cost Saving Potential in Phase II clinical Trials, 2020-2030
11.3.3. Cost Saving Potential in Phase III clinical Trials, 2020-2030
11.3.4. Cost Saving Potential in Patient Recruitment, 2020-2030
11.3.5. Cost Saving Potential in Patient Retention, 2020-2030
11.3.6. Cost Saving Potential in Site Monitoring, 2020-2030
11.3.7. Cost Saving Potential in Source Data Verification, 2020-2030

12. MARKET SIZING AND OPPORTUNITY ANALYSIS
12.1. Chapter Overview
12.2. Key Assumptions and Forecast Methodology
12.3. Overall AI-based Clinical Trial Solutions Market Opportunity, 2020-2030
12.4. AI-based Clinical Trial Solutions Market Opportunity: Distribution by Trial Phase, 2020 and 2030
12.5. AI-based Clinical Trial Solutions Market Opportunity: Distribution by Target Therapeutic Area, 2020 and 2030
12.6. AI-based Clinical Trial Solutions Market Opportunity: Distribution by End-user, 2020 and 2030
12.7. AI-based Clinical Trial Solutions Market Opportunity: Distribution by Key Geographical Regions, 2020 and 2030
12.7.1. AI-based Clinical Trial Solutions Market Opportunity in North America, 2020-2030
12.7.2. AI-based Clinical Trial Solutions Market Opportunity in Europe, 2020-2030
12.7.3. AI-based Clinical Trial Solutions Market Opportunity in Asia-Pacific, 2020-2030
12.7.4. AI-based Clinical Trial Solutions Market Opportunity in Rest of the World, 2020-2030

13. CONCLUSION
13.1. Chapter Overview
13.2. Key Takeaways

14. EXECUTIVE INSIGHTS
14.1. Chapter Overview
14.2. Intelligencia
14.2.1. Company Snapshot
14.2.2. Interview Transcript: Dimitrios Skaltsas, Co-Founder and Executive Director

15. APPENDIX I: TABULATED DATA

Companies Mentioned

A.I. VALI
AbbVie
Accenture
AccuBeing
AG Mednet
Agent Health
AiCure
Aidar Health
AliveCor
Anaqua
Anthem
Antidote
Aspen Insights
AstraZeneca
Avident Health
Bayer
Bioinfogate
BlueData
Bolton NHS Foundation Trust
Brainpan Innovations
Bristol-Myers Squibb
Brite Health
BullFrog AI
Business Health Care Group
Cambia Health Solutions
Canary Speech
Cancer Genetics
Canon
Carebox
Carenet Health
Carenity
Carnegie Mellon University
Catana Capital
Cedar Health Research
Celgene
Central Ohio Primary Care
Cerba Research
Chainlink
CHDI Foundation
ChemAxon
CIMS
Clarivate
ClinArk
Clinerion
Clinevo Technologies
Clinical AI
CliniOps
Clinithink
ClinTex
CMIC
Covance
Crestle.ai
Curify
Darts-ip
DataON
Deep 6 AI
Deep Lens
DeepTrial
Dell
Department of Veterans Affairs
DiA Imaging Analysis
doc.ai
EBSCO
Egyptian Knowledge Bank
eimageglobal
Erlanger Health System
ExperiMind Technologies
fathom it group
Flow Pharma
GE Healthcare
Genpro Research
GlaxoSmithKline
GNS Healthcare
Google
H2O.ai
Halo Health
HCL
Healint
Healthix
HealthMatch
IBM
ICON
iLoF - Intelligent Lab on Fiber
IMNA Solutions
Inato
Indegene
iNDX.Ai
Innoplexus
Inova Translational Medicine Institute
Inspire
Intel
Intelligencia.ai
Intrepid Analytics
IP Australia
IXICO
Janssen Pharmaceuticals
Johnson & Johnson
Joovv
Kadena
Kognitic
Kopernio
Kryo
Kx Systems
KYT
Leukemia & Lymphoma Society
Lieber Institute for Brain Development
Life Image
Lokavant
London Medical Imaging & Artificial Intelligence Centre
Medable
Medairum
Medaptive Health
Median Technologies
Medica
Medidata Solutions
mediri
Medtronic
Mendel.ai
Merck
MGH Group
Microsoft
Mount Sinai Health System
MRN
Nanox
NEC
Nor-Tech
Northern Data
Novadiscovery
Novartis
Novoic
nQ Medical
Olea Medical
OncoImmunity
OncoSec Medical
One Nucleus
Oura
Owkin
P360
P3Life
PangaeaData.AI
Passage AI
PatchAi
PatientPoint
PatienTrials
Patiro
Pear Therapeutics
PenRad Technologies
Pepgra
Pfizer
Pharmamodelling
PHASTAR
phaware
Phesi
Precipio
ProofPilot
protocols.io
PWNHealth
Qmetrics Technologies
QUIBIM
Qure.ai
Raylytic
Redox
Remarque Systems
Roche
Royal Philips
Rymedi
Saama Technologies
San Raffaele Hospital
Sanofi
SAP
Science37
sensedat
Sensyne Health
ServiceNow
SiteRx
Skura Corporation
Snowflake
Springer Nature
Syneos Health
Synexus
Talkdesk
Teleradiology Solutions
TeraRecon
Teva Pharmaceuticals
The ALS Association
TrademarkVision
tranScrip
Translational Drug Development
Trial Sense
Trialcome
TrialJectory
Trials.ai
TTi Health Research & Economics
University of California
University of Pennsylvania
University of Pittsburgh
Unlearn.AI
Vanguard Scientific
Veritas IRB
VIDA
Vivoryon Therapeutics
Viz.ai
Vizyon Technologies
Vooban
Wiley
Winterlight Labs
Worcestershire Health and Care NHS Trust
Worldwide Clinical Trials
Xingtai People's Hospital
KRN Scientific Consulting

For more information about this report visit https://www.researchandmarkets.com/r/mx4gmi

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