AI-based Clinical Trials Solution Provider Market Size, Share & Trends Analysis Report By Therapeutic Application, By Clinical Trial Phase, By End-use, By Region, And Segment Forecasts, 2024 - 2030

AI-based Clinical Trials Solution Provider Market Size, Share & Trends Analysis Report By Therapeutic Application, By Clinical Trial Phase, By End-use, By Region, And Segment Forecasts, 2024 - 2030


AI-based Clinical Trials Solution Provider Market Growth & Trends

The global AI-based clinical trials solution provider market size is expected to reach USD 7.8 billion by 2030, registering a CAGR of 22.1% during the forecast period, according to a new report by Grand View Research, Inc.. AI is a versatile tool and is being increasingly utilized to improve the operational efficacy of drug studies and accelerate the drug discovery process. Also, it is highly adopted to minimize the cost of the drug development process. Various academia and pharmaceutical companies are actively adopting AI-based platforms and technologies. For instance, in January 2020, Bayer entered into a partnership with an AI-driven drug discovery company based in the UK to work on early research projects using the AI drug discovery platform. Furthermore, the initiatives by the public and private sectors to boost the R&D coupled with the diverse applications provided by AI in the field of drug studies are impelling the growth.

Based on the phase of the clinical trial, Phase II dominated the market with a share of 45.5% in 2023 owing to the rising number of drug discoveries and a large number of studies active in the second phase. Moreover, the increasing adoption of AI-based tools for the collection of data and the analysis of immediate outcomes of the overall desired outcome through the clinical trials in this phase is contributing to the segment growth. Furthermore, the segment holds a higher revenue share as the improvement, determination, and validation of measures with respect to the AI-based tool can be carried out in this phase.

In 2023, based on therapeutic applications, oncology accounted for the highest revenue share in the AI-based clinical trials solution provider market. The increasing prevalence of cancer across the globe and the rising number of drug studies in the field of oncology is contributing to the adoption of AI enables technologies. Also, an increasing number of players are developing and adopting advanced oncology-based AI tools for drug studies, thereby propelling the segment growth.

In 2023, pharmaceutical companies accounted for the highest revenue share in the market, based on end-use. The rising adoption of AI-based technologies for the better development of diagnostic and biomarkers, to identify the new drug target and the overall process of drug development and drug trials by major pharmaceutical players is one of the major factors contributing to the segment growth. Moreover, these major pharmaceutical players are collaborating with the AI vendor for leveraging the AI technology for R&D and the overall drug discovery process, thereby, impelling the growth.

North America dominated the market and accounted for a revenue share of 43.4% in 2023. This dominance is attributed to the rising number of start-ups in the region. For instance, Bullfrog AI is a U.S.-based startup that develops bfLEAP, a proprietary AI platform to enable precision medicine. Also, the growing awareness of AI-based technologies and their adoption to enhance the drug studies’ outcomes is impelling the market growth in the region. Furthermore, the supportive government initiatives and increasing strategic initiatives by major players are driving the demand for AI-based clinical trial solutions in the region.

The AI-based technologies witnessed a surge in their adoption due to the outbreak of COVID-19. This increasing adoption of technologically advanced solutions for drug development and for the analyses of the recruited patient’s data contributed to the upsurge in the adoption of AI-enabled solutions. Moreover, many drug development processes were on hold during the pandemic. Therefore, many key companies in the market shifted their focus on the utilization of AI-based solutions, thereby boosting decentralized drug trials. Furthermore, the effective analysis of a large amount of patient data through these solutions supported the market growth.

AI-based Clinical Trials Solution Provider Market Report Highlights
  • The phase-II trials segment dominated the market in 2023, owing to the increasing number of active drug studies in this phase
  • The oncology segment dominated the market in 2023, owing to the rising prevalence of cancer and the growing number of drug studies in the oncology field
  • The pharmaceutical companies segment accounted for the highest revenue share in the market owing to the increasing adoption of artificial intelligence technologies for drug development by major pharmaceutical players
  • North America dominated the market and accounted for a revenue share of 43.4% in 2023, owing to various factors including the increasing number of clinical trials in the region, growing adoption of artificial intelligence platforms and tools, rising number of start-ups and companies based on artificial intelligence in drug development and growing awareness regarding AI-based tools and technologies.
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Chapter 1. Methodology and Scope
1.1. Market Segmentation & Scope
1.1.1. Therapeutic applications
1.1.2. Clinical trial phase
1.1.3. End use
1.1.4. Regional scope
1.1.5. Estimates and forecast timeline.
1.2. Research Methodology
1.3. Information Procurement
1.3.1. Purchased database.
1.3.2. GVR’s internal database
1.3.3. Secondary sources
1.3.4. Primary research
1.3.5. Details of primary research
1.4. Information or Data Analysis
1.4.1. Data analysis models
1.5. Market Formulation & Validation
1.6. Model Details
1.6.1. Commodity flow analysis (Model 1)
1.6.2. Approach 1: Commodity flow approach
1.7. List of Secondary Sources
1.8. List of Primary Sources
1.9. Objectives
Chapter 2. Executive Summary
2.1. Market Outlook
2.2. Segment Outlook
2.2.1. Therapeutic applications outlook
2.2.2. Clinical trial phase outlook
2.2.3. End use outlook
2.2.4. Regional outlook
2.3. Competitive Insights
Chapter 3. Market Variables, Trends & Scope
3.1. Market Lineage Outlook
3.1.1. Parent market outlook
3.1.2. Related/ancillary market outlook
3.2. Market Dynamics
3.2.1. Market driver analysis
3.2.2. Market restraint analysis
3.3. AI-Based Clinical Trials Solution Providers Market Analysis Tools
3.3.1. Industry Analysis - Porter’s
3.3.1.1. Supplier power
3.3.1.2. Buyer power
3.3.1.3. Substitution threat
3.3.1.4. Threat of new entrant
3.3.1.5. Competitive rivalry
3.3.2. PESTEL Analysis
3.3.2.1. Political landscape
3.3.2.2. Economic landscape
3.3.2.3. Social landscape
3.3.2.4. Technological landscape
3.3.2.5. Environmental landscape
3.3.2.6. Legal landscape
3.3.3. COVID-19 Impact
3.4. Case Studies
Chapter 4. AI-Based Clinical Trials Solution Providers Market: Therapeutic Applications Estimates & Trend Analysis
4.1. Segment Dashboard
4.2. Therapeutic Applications Movement & Market Share Analysis, 2023 & 2030
4.3. Global AI-Based Clinical Trials Solution Providers Market by Therapeutic Applications Outlook
4.4. Oncology
4.4.1. Market estimates and forecast 2018 to 2030 (USD Million)
4.5. CVD
4.5.1. Market estimates and forecast 2018 to 2030 (USD Million) CVD
4.6. Neurological Diseases or Conditions
4.6.1. Market estimates and forecast 2018 to 2030 (USD Million)
4.7. Metabolic Diseases
4.7.1. Market estimates and forecast 2018 to 2030 (USD Million)
4.8. Infectious Diseases
4.8.1. Market estimates and forecast 2018 to 2030 (USD Million)
4.9. Others
4.9.1. Market estimates and forecast 2018 to 2030 (USD Million)
Chapter 5. AI-Based Clinical Trials Solution Providers Market: Clinical Trial Phase Estimates & Trend Analysis
5.1. Segment Dashboard
5.2. Clinical Trial Phase Movement & Market Share Analysis, 2023 & 2030
5.3. Global AI-Based Clinical Trials Solution Providers Market by Clinical Trial Phase Outlook
5.4. Phase-I
5.4.1. Market estimates and forecast 2018 to 2030 (USD Million)
5.5. Phase-II
5.5.1. Market estimates and forecast 2018 to 2030 (USD Million)
5.6. Phase-III
5.6.1. Market estimates and forecast 2018 to 2030 (USD Million)
Chapter 6. AI-Based Clinical Trials Solution Providers Market: End Use Estimates & Trend Analysis
6.1. Segment Dashboard
6.2. End Use Movement & Market Share Analysis, 2023 & 2030
6.3. Global AI-Based Clinical Trials Solution Providers Market by End Use Outlook
6.4. Pharmaceutical Companies
6.4.1. Market estimates and forecast 2018 to 2030 (USD Million)
6.5. Academia
6.5.1. Market estimates and forecast 2018 to 2030 (USD Million)
6.6. Others
6.6.1. Market estimates and forecast 2018 to 2030 (USD Million)
Chapter 7. AI-Based Clinical Trials Solution Providers Market: Regional Estimates & Trend Analysis, By Therapeutic Applications, By Clinical Trial Phase, By End Use
7.1. Regional Market Share Analysis, 2023 & 2030
7.2. Regional Market Dashboard
7.3. Global Regional Market Snapshot
7.4. Market Size, & Forecasts Trend Analysis, 2018 to 2030:
7.5. North America
7.5.1. U.S.
7.5.1.1. Key country dynamics
7.5.1.2. Regulatory framework/ reimbursement structure
7.5.1.3. Competitive scenario
7.5.1.4. U.S. market estimates and forecasts 2018 to 2030 (USD Million)
7.5.2. Canada
7.5.2.1. Key country dynamics
7.5.2.2. Regulatory framework/ reimbursement structure
7.5.2.3. Competitive scenario
7.5.2.4. Canada market estimates and forecasts 2018 to 2030 (USD Million)
7.6. Europe
7.6.1. UK
7.6.1.1. Key country dynamics
7.6.1.2. Regulatory framework/ reimbursement structure
7.6.1.3. Competitive scenario
7.6.1.4. UK market estimates and forecasts 2018 to 2030 (USD Million)
7.6.2. Germany
7.6.2.1. Key country dynamics
7.6.2.2. Regulatory framework/ reimbursement structure
7.6.2.3. Competitive scenario
7.6.2.4. Germany market estimates and forecasts 2018 to 2030 (USD Million)
7.6.3. France
7.6.3.1. Key country dynamics
7.6.3.2. Regulatory framework/ reimbursement structure
7.6.3.3. Competitive scenario
7.6.3.4. France market estimates and forecasts 2018 to 2030 (USD Million)
7.6.4. Italy
7.6.4.1. Key country dynamics
7.6.4.2. Regulatory framework/ reimbursement structure
7.6.4.3. Competitive scenario
7.6.4.4. Italy market estimates and forecasts 2018 to 2030 (USD Million)
7.6.5. Spain
7.6.5.1. Key country dynamics
7.6.5.2. Regulatory framework/ reimbursement structure
7.6.5.3. Competitive scenario
7.6.5.4. Spain market estimates and forecasts 2018 to 2030 (USD Million)
7.6.6. Norway
7.6.6.1. Key country dynamics
7.6.6.2. Regulatory framework/ reimbursement structure
7.6.6.3. Competitive scenario
7.6.6.4. Norway market estimates and forecasts 2018 to 2030 (USD Million)
7.6.7. Sweden
7.6.7.1. Key country dynamics
7.6.7.2. Regulatory framework/ reimbursement structure
7.6.7.3. Competitive scenario
7.6.7.4. Sweden market estimates and forecasts 2018 to 2030 (USD Million)
7.6.8. Denmark
7.6.8.1. Key country dynamics
7.6.8.2. Regulatory framework/ reimbursement structure
7.6.8.3. Competitive scenario
7.6.8.4. Denmark market estimates and forecasts 2018 to 2030 (USD Million)
7.7. Asia Pacific
7.7.1. Japan
7.7.1.1. Key country dynamics
7.7.1.2. Regulatory framework/ reimbursement structure
7.7.1.3. Competitive scenario
7.7.1.4. Japan market estimates and forecasts 2018 to 2030 (USD Million)
7.7.2. China
7.7.2.1. Key country dynamics
7.7.2.2. Regulatory framework/ reimbursement structure
7.7.2.3. Competitive scenario
7.7.2.4. China market estimates and forecasts 2018 to 2030 (USD Million)
7.7.3. India
7.7.3.1. Key country dynamics
7.7.3.2. Regulatory framework/ reimbursement structure
7.7.3.3. Competitive scenario
7.7.3.4. India market estimates and forecasts 2018 to 2030 (USD Million)
7.7.4. Australia
7.7.4.1. Key country dynamics
7.7.4.2. Regulatory framework/ reimbursement structure
7.7.4.3. Competitive scenario
7.7.4.4. Australia market estimates and forecasts 2018 to 2030 (USD Million)
7.7.5. South Korea
7.7.5.1. Key country dynamics
7.7.5.2. Regulatory framework/ reimbursement structure
7.7.5.3. Competitive scenario
7.7.5.4. South Korea market estimates and forecasts 2018 to 2030 (USD Million)
7.7.6. Thailand
7.7.6.1. Key country dynamics
7.7.6.2. Regulatory framework/ reimbursement structure
7.7.6.3. Competitive scenario
7.7.6.4. Singapore market estimates and forecasts 2018 to 2030 (USD Million)
7.8. Latin America
7.8.1. Brazil
7.8.1.1. Key country dynamics
7.8.1.2. Regulatory framework/ reimbursement structure
7.8.1.3. Competitive scenario
7.8.1.4. Brazil market estimates and forecasts 2018 to 2030 (USD Million)
7.8.2. Mexico
7.8.2.1. Key country dynamics
7.8.2.2. Regulatory framework/ reimbursement structure
7.8.2.3. Competitive scenario
7.8.2.4. Mexico market estimates and forecasts 2018 to 2030 (USD Million)
7.8.3. Argentina
7.8.3.1. Key country dynamics
7.8.3.2. Regulatory framework/ reimbursement structure
7.8.3.3. Competitive scenario
7.8.3.4. Argentina market estimates and forecasts 2018 to 2030 (USD Million)
7.9. MEA
7.9.1. South Africa
7.9.1.1. Key country dynamics
7.9.1.2. Regulatory framework/ reimbursement structure
7.9.1.3. Competitive scenario
7.9.1.4. South Africa market estimates and forecasts 2018 to 2030 (USD Million)
7.9.2. Saudi Arabia
7.9.2.1. Key country dynamics
7.9.2.2. Regulatory framework/ reimbursement structure
7.9.2.3. Competitive scenario
7.9.2.4. Saudi Arabia market estimates and forecasts 2018 to 2030 (USD Million)
7.9.3. UAE
7.9.3.1. Key country dynamics
7.9.3.2. Regulatory framework/ reimbursement structure
7.9.3.3. Competitive scenario
7.9.3.4. UAE market estimates and forecasts 2018 to 2030 (USD Million)
7.9.4. Kuwait
7.9.4.1. Key country dynamics
7.9.4.2. Regulatory framework/ reimbursement structure
7.9.4.3. Competitive scenario
7.9.4.4. Kuwait market estimates and forecasts 2018 to 2030 (USD Million)
Chapter 8. Competitive Landscape
8.1. Recent Developments & Impact Analysis, By Key Market Participants
8.2. Company/Competition Categorization
8.3. Key company market share/position analysis, 2023
8.4. Company Profiles
8.4.1. Unlearn.ai, Inc.
8.4.1.1. Company overview
8.4.1.2. Financial performance
8.4.1.3. Technology Type benchmarking
8.4.1.4. Strategic initiatives
8.4.2. Saama
8.4.2.1. Company overview
8.4.2.2. Financial performance
8.4.2.3. Technology Type benchmarking
8.4.2.4. Strategic initiatives
8.4.3. Antidote Technologies, Inc.
8.4.3.1. Company overview
8.4.3.2. Financial performance
8.4.3.3. Technology Type benchmarking
8.4.3.4. Strategic initiatives
8.4.4. Phesi
8.4.4.1. Company overview
8.4.4.2. Financial performance
8.4.4.3. Technology Type benchmarking
8.4.4.4. Strategic initiatives
8.4.5. Deep6.ai
8.4.5.1. Company overview
8.4.5.2. Financial performance
8.4.5.3. Technology Type benchmarking
8.4.5.4. Strategic initiatives
8.4.6. Innoplexus
8.4.6.1. Company overview
8.4.6.2. Financial performance
8.4.6.3. Technology Type benchmarking
8.4.6.4. Strategic initiatives
8.4.7. Mendel Health Inc.
8.4.7.1. Company overview
8.4.7.2. Financial performance
8.4.7.3. Technology Type benchmarking
8.4.7.4. Strategic initiatives
8.4.8. Intelligencia AI
8.4.8.1. Company overview
8.4.8.2. Financial performance
8.4.8.3. Technology Type benchmarking
8.4.8.4. Strategic initiatives
8.4.9. Median Technologies
8.4.9.1. Company overview
8.4.9.2. Financial performance
8.4.9.3. Technology Type benchmarking
8.4.9.4. Strategic initiatives
8.4.10. SymphonyAI
8.4.10.1. Company overview
8.4.10.2. Financial performance
8.4.10.3. Technology Type benchmarking
8.4.10.4. Strategic initiatives
8.4.11. BioAge Labs, Inc.
8.4.11.1. Company overview
8.4.11.2. Financial performance
8.4.11.3. Technology Type benchmarking
8.4.11.4. Strategic initiatives
8.4.12. AiCure
8.4.12.1. Company overview
8.4.12.2. Financial performance
8.4.12.3. Technology Type benchmarking
8.4.12.4. Strategic initiatives
8.4.13. Consilx
8.4.13.1. Company overview
8.4.13.2. Financial performance
8.4.13.3. Technology Type benchmarking
8.4.13.4. Strategic initiatives
8.4.14. DeepLens.AI
8.4.14.1. Company overview
8.4.14.2. Financial performance
8.4.14.3. Technology Type benchmarking
8.4.14.4. Strategic initiatives
8.4.15. HaloHealth
8.4.15.1. Company overview
8.4.15.2. Financial performance
8.4.15.3. Technology Type benchmarking
8.4.15.4. Strategic initiatives
8.4.16. PHARMASEAL
8.4.16.1. Company overview
8.4.16.2. Financial performance
8.4.16.3. Technology Type benchmarking
8.4.16.4. Strategic initiatives
8.4.17. Ardigen
8.4.17.1. Company overview
8.4.17.2. Financial performance
8.4.17.3. Technology Type benchmarking
8.4.17.4. Strategic initiatives
8.4.18. Trials.ai
8.4.18.1. Company overview
8.4.18.2. Financial performance
8.4.18.3. Technology Type benchmarking
8.4.18.4. Strategic initiatives
8.4.19. Koneksa Health
8.4.19.1. Company overview
8.4.19.2. Financial performance
8.4.19.3. Technology Type benchmarking
8.4.19.4. Strategic initiatives
8.4.20. Euretos
8.4.20.1. Company overview
8.4.20.2. Financial performance
8.4.20.3. Technology Type benchmarking
8.4.20.4. Strategic initiatives
8.4.21. BioSymetrics, Inc.
8.4.21.1. Company overview
8.4.21.2. Financial performance
8.4.21.3. Technology Type benchmarking
8.4.21.4. Strategic initiatives
8.4.22. Verily (Google LLC)
8.4.22.1. Company overview
8.4.22.2. Financial performance
8.4.22.3. Technology Type benchmarking
8.4.22.4. Strategic initiatives
8.4.23. Aitia
8.4.23.1. Company overview
8.4.23.2. Financial performance
8.4.23.3. Technology Type benchmarking
8.4.23.4. Strategic initiatives
8.4.24. IBM
8.4.24.1. Company overview
8.4.24.2. Financial performance
8.4.24.3. Technology Type benchmarking
8.4.24.4. Strategic initiatives
8.4.25. Exscientia
8.4.25.1. Company overview
8.4.25.2. Financial performance
8.4.25.3. Technology Type benchmarking
8.4.25.4. Strategic initiatives

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