Generative AI In Clinical Trials Market Size
| Study Period | 2019 - 2029 |
| Market Size (2024) | USD 199.35 Billion |
| Market Size (2029) | USD 565.18 Billion |
| CAGR (2024 - 2029) | 23.20 % |
| Fastest Growing Market | Asia Pacific |
| Largest Market | North America |
| Market Concentration | Medium |
Major Players
*Disclaimer: Major Players sorted in no particular order |
Generative AI In Clinical Trials Market Analysis
The Generative AI In Clinical Trials Market size is estimated at USD 199.35 billion in 2024, and is expected to reach USD 565.18 billion by 2029, growing at a CAGR of 23.20% during the forecast period (2024-2029).
Factors such as enhanced drug discovery and development through AI-driven innovation, the role of AI in clinical research, and growing technological advancements are expected to boost market growth over the forecast period.
The rising global prevalence of chronic diseases highlights the urgent demand for sophisticated clinical trial management systems. These systems are essential for efficiently navigating and optimizing the increasingly intricate processes of clinical trials, thereby driving growth in the generative AI in clinical trials market.
For instance, an article from ClinicalTrials.gov in January 2024 noted a significant increase in registered clinical trial studies, globally, from 399,484 in 2022 to 478,855 in 2024. This increase underscores the expanding scale and importance of clinical research. Given this exponential rise in clinical trials, a surge is anticipated in demand for AI generative tools. These tools are pivotal in streamlining various clinical trial phases, such as patient recruitment, data analysis, and trial design. As the volume of trials increases, the urgency for these efficiencies amplifies, further fueling market expansion.
Rapid advancements in artificial intelligence, especially generative AI (GenAI), are accelerating technology adoption and transforming the healthcare industry. This widespread accessibility of AI capabilities paves the way for innovation in clinical trials and drug development, marking a shift from previous technological advancements.
For instance, a July 2024 article in Clinical Research highlighted the value of GenAI in expediting trial design and execution. By analyzing historical trial data, GenAI can rapidly draft protocols, automate document creation, and adjust trials in real time based on new data. Furthermore, it promotes informed, data-driven decisions throughout the clinical trial lifecycle. Given these advantages, the anticipated surge in GenAI adoption in clinical research and trials is set to drive significant market growth in the coming years.
Moreover, the growing adoption of generative AI in drug discovery and development is set to drive market growth during the forecast period. For instance, in June 2023, Insilico Medicine, a clinical-stage biotechnology company harnessing generative AI, administered the first dose in the Phase II clinical trial of INS018_055. This milestone marked the debut of the first anti-fibrotic small molecule inhibitor, both discovered and designed through generative AI, entering Phase II clinical trials for further assessment.
Furthermore, the same source reported that Insilico's AI-discovered and AI-generated drug, INS018_055, is undergoing multi-regional Phase II clinical trials in both the United States and China. Hence, the advancements of generative AI in drug discovery and development are poised to significantly enhance the market for generative AI in clinical trials during the forecast period.
Therefore, owing to the rising burden of chronic diseases creating a need for generative AI tools for managing large amounts of clinical trial data and growing adoption of Gen-AI in clinical trials for new drug discovery and development, clinical trial design is expected to boost the growth of the market. However, the requirements for data privacy, security, and regulatory compliance are likely to hinder the growth of the generative AI in clinical trials market over the forecast period.
Generative AI In Clinical Trials Market Trends
Clinical Trial Design Segment Expected to Witness Significant Growth Over the Forecast Period
Machine learning, a subset of AI, is transforming clinical research, and these areas include clinical trial design, digital health technologies, and real-world data analytics. Due to its ability to enhance strategic planning and execution phases, the clinical trial design segment is poised to drive the growth of generative AI in the clinical trials market. Furthermore, there is a growing reliance on AI-driven methodologies to streamline research protocols and refine health outcome precision.
Clinical trials are pivotal in pharmaceutical development, serving as the benchmark for validating new drugs' safety and efficacy. Generative AI (Gen AI) is a transformative technology poised to revolutionize clinical trials by refining trial designs and optimizing patient recruitment. Numerous studies have delved into generative AI's potential in refining trial designs and enhancing patient recruitment.
For instance, a May 2024 article in Clinical Research highlighted that generative AI leverages electronic health records (EHRs), social media activities, and diverse data sources to identify and recruit optimal clinical trial candidates. Moreover, AI algorithms outpace traditional methods in matching patient profiles with trial criteria, leading to heightened recruitment rates and expedited enrollment.
According to the aforementioned source, Gen AI models possess the capability to simulate diverse trial designs, identifying the most effective protocols. By scrutinizing historical trial data and forecasting potential outcomes, AI empowers researchers to design trials with a higher likelihood of success. This encompasses decisions on optimal sample sizes, treatment regimens, and endpoints. Such capabilities not only foster more efficient and adaptive clinical trials but are also expected to bolster the anticipated growth of generative AI in clinical trial designs over the forecast period.
Furthermore, the launches of new generative AI platforms for advancing and maintaining clinical trial information are anticipated to bolster the segment’s growth over the forecast period. For instance, Clarivate PLC launched a new enhanced search platform leveraging generative artificial intelligence (GenAI). The new Clarivate offering enables drug discovery, pre-clinical, clinical, regulatory affairs, and portfolio strategy teams to interact with multiple complex datasets using natural language to obtain immediate and in-depth insights.
Therefore, the rising adoption of generative AI in designing clinical trials is expected to augment the growth of the segment over the forecast period.
North America Expected to Hold a Significant Market Share Over the Forecast Period
North America is poised for substantial growth during the forecast period. This surge can be attributed to several factors, such as the increasing demand for expedited and efficient clinical trial processes to introduce new therapies, advancements in sophisticated AI algorithms bolstered by enhanced computational power, and a rising collaboration between AI companies, research institutions, and pharmaceutical companies in the region.
The rise of generative AI (GenAI) in the clinical trial arena stands out as the primary catalyst for this market growth. For instance, an April 2024 article in Applied Clinical Trials highlighted that GenAI is poised to steer the clinical development model toward a more data-driven and patient-centric direction. This shift is set to revolutionize the current clinical development approach, especially given the surge in readily available real-world data and the escalating resource demands of clinical trials, all in response to the rapid pace of novel drug discoveries driven by GenAI.
In addition, GenAI algorithms for decision-making would create higher values if substantiated in validations and become operational across all functions and stages of clinical developments ranging from drug discovery and pre-clinical study to clinical trials. Thus, the current clinical development model is shifting toward a GenAI-augmented proactive approach, supported by real-world data for real-time evidence.
The integration of GenAI in clinical trials has been found to shorten the decision lag time, and clinical trials would be more insightful in clinical contexts as the full spectrum of relevant variables would be fully embedded in the GenAI algorithm for greater efficiency and performance improvements, accelerating clinical developments from pre-clinical to clinical stages with substantial cost-saving. Hence, such developments are expected to boost market growth over the forecast period.
Furthermore, the increasing introduction of advanced generative AI tools aimed at expediting clinical trials is poised to boost their adoption, subsequently driving market growth during the forecast period. For instance, in October 2023, H1 unveiled GenosAI, a new generative AI tool integrated into its clinical trial intelligence platform, Trial Landscape. This tool is designed to analyze and address a wide range of complex inquiries. Trial Landscape empowers sponsors to identify new sites, identify centers of excellence, and select investigators with diverse patient backgrounds. It also aids in assessing site and principal investigator recruitment strategies and evaluating competition across various trials.
Moreover, the emergence of innovative generative AI models for drug development is set to fuel market growth in the coming years. For instance, in March 2024, a collaboration between Stanford Medicine and McMaster University developed SyntheMol, an AI model that generates synthesis recipes for chemists. This model targets the creation of six novel drugs designed to combat resistant strains of Acinetobacter baumannii in laboratory settings. Such advancements are likely to motivate companies to further innovate in generative AI models for drug discovery, thereby accelerating market growth.
Therefore, with technological advancements, new product launches by key players, and the development of novel GenAI models by researchers, the market is expected to grow over the forecast period.
Generative AI In Clinical Trials Industry Overview
The generative AI in clinical trials market is typically fragmented owing to the presence of a few major technology companies and specialized startups. Several key players are applying AI to drug discovery and clinical trials. Significant investments and strategic partnerships between AI firms and pharmaceutical companies also drive market concentration. Some of the players are leveraging advanced technologies for drug development and clinical trials. Some of the key players in the market are IBM, Tempus, Microsoft Corporation, H1, NVIDIA, and Exscientia.
Generative AI In Clinical Trials Market Leaders
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IBM
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Tempus
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Exscientia
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NVIDA
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H1
*Disclaimer: Major Players sorted in no particular order
Generative AI In Clinical Trials Market News
- July 2024: Exscientia PLC deepened its collaboration with Amazon Web Services (AWS), leveraging AWS' artificial intelligence (AI) and machine learning (ML) services to enhance its comprehensive drug discovery and automation platform. Utilizing generative AI models, Exscientia's platform harnesses the scalability and flexibility of AWS. This collaboration enables the rapid, secure, and efficient design of drug candidates, aiming to more precisely target specific diseases and patients, thereby expediting early drug development while reducing costs.
- May 2024: ConcertAI introduced predictive and generative AI solutions and a clinical oncology suite to enhance research capabilities and support complex clinical study workflows. These solutions provide researchers with enhanced data analysis tools for in-depth study, contributing to more informed care strategies that can improve patient outcomes. The company's CARA AI is a multi-modal data management, predictive AI, and generative AI platform that can accelerate research from translational through clinical development and support multi-party collaborations.
Generative AI In Clinical Trials Market Report - Table of Contents
1. INTRODUCTION
1.1 Study Assumptions and Market Definition
1.2 Scope of the Study
2. RESEARCH METHODOLOGY
3. EXECUTIVE SUMMARY
4. MARKET DYNAMICS
4.1 Market Overview
4.2 Market Drivers
4.2.1 Enhancing Drug Discovery and Development Through AI-Driven Innovation
4.2.2 Role of AI in Clinical Research
4.2.3 Growing Technological Advancements
4.3 Market Restraints
4.3.1 Data Privacy and Security
4.3.2 Regulatory Compliance Requirements
4.4 Porter's Five Forces Analysis
4.4.1 Threat of New Entrants
4.4.2 Bargaining Power of Buyers/Consumers
4.4.3 Bargaining Power of Suppliers
4.4.4 Threat of Substitute Products
4.4.5 Intensity of Competitive Rivalry
5. MARKET SEGMENTATION (Market Size by Value - USD)
5.1 By Application
5.1.1 Data Generation
5.1.2 Clinical Trial Design
5.1.3 Outcome Prediction
5.1.4 Adverse Event Detection
5.1.5 Other Applications (Data Imputation and Denoising)
5.2 By Technology
5.2.1 Variational Autoencoders (VAEs)
5.2.2 Generative Adversarial Networks (GAN)
5.2.3 Deep Convolutional Networks (DCNs)
5.2.4 Transfer Learning
5.2.5 Other Technologies (Machine Learning and Natural Language Processing (NLP))
5.3 By End Users
5.3.1 Researchers and Scientists
5.3.2 Clinical Trial Sponsors and CROs
5.3.3 Other End Users (Data Analysts and Biostatisticians and Healthcare Professionals)
5.4 Geography
5.4.1 North America
5.4.1.1 United States
5.4.1.2 Canada
5.4.1.3 Mexico
5.4.2 Europe
5.4.2.1 Germany
5.4.2.2 United Kingdom
5.4.2.3 France
5.4.2.4 Italy
5.4.2.5 Spain
5.4.2.6 Rest of Europe
5.4.3 Asia-Pacific
5.4.3.1 China
5.4.3.2 Japan
5.4.3.3 India
5.4.3.4 Australia
5.4.3.5 South Korea
5.4.3.6 Rest of Asia-Pacific
5.4.4 Middle East and Africa
5.4.4.1 GCC
5.4.4.2 South Africa
5.4.4.3 Rest of Middle East and Africa
5.4.5 South America
5.4.5.1 Brazil
5.4.5.2 Argentina
5.4.5.3 Rest of South America
6. COMPETITIVE LANDSCAPE
6.1 Company Profiles
6.1.1 IBM
6.1.2 Tempus
6.1.3 Benevolent AI
6.1.4 Exscientia
6.1.5 Deep Genomics
6.1.6 NVIDA
6.1.7 H1
6.1.8 ConcertAI
6.1.9 Tencent Holdings Ltd
- *List Not Exhaustive
7. MARKET OPPORTUNITIES AND FUTURE TRENDS
Generative AI In Clinical Trials Industry Segmentation
As per the scope of the report, generative AI utilizes algorithms to produce new data or content derived from pre-existing data. These AI systems learn from extensive datasets and designed predictive models that aid in diverse applications, such as clinical research. Generative AI aids in the optimization of clinical trial design and protocol development. The technology simulates various trial scenarios, considering diverse factors such as patient demographics, treatment regimens, and potential variations in study outcomes.
The generative AI in clinical trials market is segmented by application, technology, end user, and geography. By application, the market is segmented into data generation, clinical trial design, outcome prediction, adverse event detection, and other applications. The other applications segment comprises data imputation and denoising, and outcome prediction. By technology, the market is segmented into variational autoencoders (VAEs), generative adversarial networks (GAN), deep convolutional networks (DCNs), transfer learning, and other technologies. The other technologies segment comprises machine learning and and natural language processing (NLP). By end user, the market is segmented into researchers and scientists, clinical trial sponsors and CROs, and other end users. The other end users segment comprises data analysts and biostatisticians and healthcare professionals. By geography, the market is segmented into North America, Europe, Asia-Pacific, Middle East and Africa, and South America. For each segment, the market sizing and forecasts have been done on the basis of value (in USD).
| By Application | |
| Data Generation | |
| Clinical Trial Design | |
| Outcome Prediction | |
| Adverse Event Detection | |
| Other Applications (Data Imputation and Denoising) |
| By Technology | |
| Variational Autoencoders (VAEs) | |
| Generative Adversarial Networks (GAN) | |
| Deep Convolutional Networks (DCNs) | |
| Transfer Learning | |
| Other Technologies (Machine Learning and Natural Language Processing (NLP)) |
| By End Users | |
| Researchers and Scientists | |
| Clinical Trial Sponsors and CROs | |
| Other End Users (Data Analysts and Biostatisticians and Healthcare Professionals) |
| Geography | ||||||||
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Generative AI In Clinical Trials Market Research FAQs
How big is the Generative AI In Clinical Trials Market?
The Generative AI In Clinical Trials Market size is expected to reach USD 199.35 billion in 2024 and grow at a CAGR of 23.20% to reach USD 565.18 billion by 2029.
What is the current Generative AI In Clinical Trials Market size?
In 2024, the Generative AI In Clinical Trials Market size is expected to reach USD 199.35 billion.
Who are the key players in Generative AI In Clinical Trials Market?
IBM, Tempus, Exscientia, NVIDA and H1 are the major companies operating in the Generative AI In Clinical Trials Market.
Which is the fastest growing region in Generative AI In Clinical Trials Market?
Asia Pacific is estimated to grow at the highest CAGR over the forecast period (2024-2029).
Which region has the biggest share in Generative AI In Clinical Trials Market?
In 2024, the North America accounts for the largest market share in Generative AI In Clinical Trials Market.
What years does this Generative AI In Clinical Trials Market cover, and what was the market size in 2023?
In 2023, the Generative AI In Clinical Trials Market size was estimated at USD 153.10 billion. The report covers the Generative AI In Clinical Trials Market historical market size for years: 2019, 2020, 2021, 2022 and 2023. The report also forecasts the Generative AI In Clinical Trials Market size for years: 2024, 2025, 2026, 2027, 2028 and 2029.
Generative AI In Clinical Trials Industry Report
Statistics for the 2024 Generative AI In Clinical Trials market share, size and revenue growth rate, created by Mordor Intelligence™ Industry Reports. Generative AI In Clinical Trials analysis includes a market forecast outlook for 2024 to 2029 and historical overview. Get a sample of this industry analysis as a free report PDF download.
