Artificial Intelligence Based Clinical Trials Market By Offering (Software {Clinical Trial Management Systems (CTMS), Electronic Data Capture (EDC), Predictive Analytics Tools, Patient Matching Platforms}, Services {AI-Powered Trial Design, Data Analysis & Interpretation Patient Recruitment & Retention, Site Selection & Monitoring}), By Process (Trial Design, Patient Selection, Site Selection, Patient Monitoring), By Clinical Trial Phase (Phase I, Phase II, Phase III), By Therapeutic Application (Oncology, Cardiology, Neurology, Infectious Diseases, Others, By End-User (Pharmaceutical Companies, Biotechnology Firms, Contract Research Organizations (CROs), Academic & Research Institutes), Global Market Size, Segmental analysis, Regional Overview, Company share analysis, Leading Company Profiles and Market Forecast, 2025 – 2035

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Industry Outlook

The Artificial Intelligence Based Clinical Trials Market accounted for USD 2.15 Billion in 2024 and USD 2.67 Billion in 2025 is expected to reach USD 23.63 Billion by 2035, growing at a CAGR of around 24.35% between 2025 and 2035. Artificial Intelligence is incorporated into each step of a clinical trial, like patient recruitment, designing the trial, observing and analyzing the data. They allow researchers to find out who should be involved in the study earlier, design effective trial protocols and better interpret the data they are given. More companies are using AI in clinical trials because the development of drugs now needs to be faster, less costly and more accurate. Because more companies and health organizations are turning to digital advances, AI is assisting researchers in clinical studies by helping minimize trial failures and improving how things are done.

Industry Experts Opinion

“Technology is no longer the question. The questions that remain are the ‘should we’ questions, the questions of security, privacy, ethics, and compliance. The governance framework is missing in so many places right now.”

• Brian Martin – Head of AI, AbbVie

Report Scope:

Market Dynamics

The growing use of artificial intelligence in clinical trials is driven by its ability to accelerate patient recruitment, reduce trial duration, and enhance data accuracy.

AI is being incorporated into clinical trials mostly because it hastens the process of finding and enrolling study participants which often takes a lot of time and money. AI helps review a huge amount of patient data to find those who meet the requirements for different clinical trials. With targeted recruitment, researchers can quickly fill their studies using suitable individuals who may make the study more likely to succeed. With AI, there is less delay in screening patients and checking charts, so both companies and research organisations can introduce new medicines onto the market more quickly.

AI reduces the time it takes for a clinical trial to end and the accuracy of the collected data in addition to helping find patients for trials. AI tools can track patients’ progress as they go and immediately detect if anything is amiss with the data. By doing this, researchers can avoid errors in their data that could have bad results in the trials. Using AI in predictive analytics helps researchers anticipate trial results or pinpoint upcoming issues early enough to act quickly. Both time and costs are kept to a minimum while clinical trial outcomes are made more reliable and of better quality.

The increasing availability of large healthcare datasets from electronic health records, wearables, and genomics is empowering AI to optimize trial design and patient monitoring.

With data from sources such as electronic health records, wearables and genome analysis, artificial intelligence can now strongly shape new clinical trials. The information found in these datasets supports AI in analyzing patients’ histories, different types of diseases and the results of their treatments. This data allows AI to customize clinical trials for various patient groups which can help guarantee the success of each trial. Due to data, researchers can forecast the results of treatment for different groups of patients, establish appropriate guidelines for selecting and excluding patients and set specific endpoints.

Historical data helps find better results faster, simplifies the planning stage and lowers the time it takes to carry out clinical studies. Pharmaceutical companies and research organizations can greatly benefit from AI since it allows them to predict the result of a trial before it starts. Along with designing the trial, the meaningful data collected by real-time monitoring tools and DNA sequencing is improving the surveillance of patients during testing. An AI can review data from smartwatches, fitness trackers and biosensors to notice early signs, side effects or changes in a person’s health. This way of monitoring patients guarantees their well-being and gathers the necessary information for examination.

When AI is added to genomic information, it helps scientists better understand the effects of genetic variations on a person’s response to drugs. For this reason, researchers can immediately decide what actions to take, promptly intervene when required and update the treatment or trial plan to enhance disparity management. As a result, the use of digital health data and AI is allowing clinical trials to be developed more intelligently, quickly and flexibly to address the various health issues people face.

High implementation costs and a shortage of skilled professionals in AI and healthcare analytics are limiting the widespread adoption of AI in clinical trials.

The use of AI in clinical trials is being held back by its high cost and lack of professionals with the necessary skills. Applying AI to clinical research involves buying modern software, upgrading technology for data storage and cloud services and ensuring the safety of sensitive information. Many small and medium-sized pharmaceutical companies struggle to cover the initial costs involved. Many old systems have to be updated or replaced to support AI, meaning the costs increase. Regular maintenance, sticking to regulations and protecting data privacy are other expenses that companies deal with. Because of these financial issues, some companies are reluctant to try AI due to its future advantages.

One more serious challenge is that there are not enough people who are skilled at joining both the healthcare and AI worlds. AI can only be successfully applied in clinical trials when the key medical, statistical and regulatory matters are deeply understood. There are not enough specialists knowledgeable in machine learning, data science and medical research to fill the available jobs. It results in a slower introduction of AI tools to be used for patient care. It is challenging for organizations to bring together workers from different fields to apply technology in healthcare.

The absence of well-trained workers increases the risk of incorrectly interpreting health data, disobeying health rules and raising ethical questions. Because of these limits, there is less trust and acceptance of AI technology in clinical research. The industry can overcome these restrictions by using cost-effective strategies and increasing the number of training programs for employees working with AI in healthcare.

The rise of decentralized and virtual clinical trials post-COVID has opened up strong demand for AI-based remote monitoring and real-time data analytics tools.

Because of the introduction of decentralized and virtual trials after COVID-19, there has been a big chance for AI-supported remote monitoring and immediate data analysis to be used. Due to delays in on-site medical trials during the pandemic, the industry moved fast to launch trials where patients could take part at home. So medical institutions now need technologies that check patients’ health online, gather observations remotely and instantly review them, so medical visits at clinical sites are kept to a minimum. Processing and understanding the huge amount of data from wearables, telehealth tools, mobile apps and similar remote systems relies on artificial intelligence.

With AI, data from patients is checked constantly so anomalies can be spotted, the progress of treatment can be tracked and any urgent matters are brought to the team’s attention, both improving security and the trial’s efficiency. Because of these tools, trial protocols can be modified for each participant using their data. AI also allows for automating messages, notices and follow-up tasks which helps patients follow good practices and complete the study faster. Such tools are now attracting more support from companies and CROs to ensure that patients living far from hospitals have better access to clinical trials.

The increased use of AI in virtual clinical environments is expected to greatly benefit technology companies and could also change how trials are conducted. When additional regulatory bodies support digital health, the use of AI in decentralized trials is certain to become even more common, making AI an important part of the industry long after the pandemic.

Increasing partnerships between pharmaceutical companies, AI startups, and academic institutions are paving the way for more efficient and adaptive trial models.

The combination of leading pharmaceutical groups with AI startups and universities is supporting the design of improved, flexible and intelligent models for clinical trials. They are allowing academic institutions to join with AI startups and pharma firms to use their collective knowledge and technology. In essence, they are pioneering AI-powered platforms to optimize every step of a clinical trial such as finding participants, performing research, designing a trial and modelling results. They also save money and time in clinical trials because they allow for smarter decisions and automated tasks. Educational institutions in AI and healthcare perform leading studies, while startups give new ideas and inventive methods. Pharmaceutical companies provide money, actual patient information and support for conducting trials.

Collaborating with different companies, AI technologies are being designed and tested in various real-world situations which helps them become more accurate and ready for regulations. Further, forming such partnerships usually results in the creation of shared tools for data management and new approaches to fostering innovation, helping both parties exchange knowledge more efficiently. Because of the expanding interest in personalized medicine and precision trials, the partnerships support the creation of trial models that are both speedier and more economical, as well as centred on patients and their inclusivity. The link between different sectors should lead to numerous changes in clinical trials and help the Artificial Intelligence-Based Clinical Trials Market grow in the next few years.

Segment Analysis

Based on Offering, the Artificial Intelligence Based Clinical Trials Market is segmented into Software and Services. The Software segment has Clinical Trial Management Systems (CTMS), Electronic Data Capture (EDC) and Predictive Analytics Tools, as well as Patient Matching Platforms, while the Services segment consists of AI-Powered Trial Design, Data Analysis & Interpretation, Patient Recruitment & Retention and Site Selection & Monitoring. The Software part of the market is leading nowadays since new AI-driven platforms are being used to make clinical trial management simpler, reduce errors and improve the way patients are matched. Such software ensures automated, scalable and well-integrated processes which help pharmaceutical firms make their trials both faster and less expensive.

Based on Process, the Artificial Intelligence Based Clinical Trials Market is segmented into Trial Design, Patient Selection, Site Selection, and Patient Monitoring. The Patient Selection segment is playing the leading position in the current Artificial Intelligence-Based Clinical Trials market, according to process segmentation. AI can review countless records, including those from patients’ medical charts, their genes and clinical evidence, much faster to help target trial candidates accurately. When AI is used for patient selection, studies finish faster and more successfully which is why it is the most widely used application among the processes.

Regional Analysis

A combination of a modern technological frame, a progressing pharmaceutical industry and a broad uptake of digital health systems is leading to fast growth in the North American Artificial Intelligence-Based Clinical Trials Market. Many biotech enterprises, research institutions and AI companies in the U.S. are collaborating to advance how efficient and accurate clinical trials are. Additional funding and helpful laws for AI in healthcare are boosting the pace of progress. Using electronic medical records and real-life data, it is now possible to find suitable patients and predict their needs. The market is also benefiting from increased financial support from governments and private companies in AI for healthcare. Well-known players in the Asia-Pacific region are making efforts to speed up trial processes and enhance patient results by applying machine learning and natural language processing.

Increased digitization in healthcare, more R&D spending and greater pharmaceutical activity in the Asia Pacific Artificial Intelligence-Based Clinical Trials are growing rapidly. Some countries, for instance, China, India, Japan and South Korea, are introducing AI to boost the modernization of their clinical trials. There are many patients in Asia, making it appealing for global companies to conduct studies there. Public institutions and healthcare companies are starting to rely on digital health services, as well as AI-driven tools and telemedicine to increase how accessible trials are. The market is progressing because of the growing number of AI startups and more partnerships among different countries. Because technology is advancing in the region so quickly at lower costs, AI-backed medical research is likely to have a bright future there.

Competitive Landscape

In the Artificial Intelligence-Based Clinical Trials Market, big pharmaceutical firms, AI businesses and newly founded companies all join efforts to improve the field of clinical research. AI is now being used by leading companies such as IBM Watson Health, Deep 6 AI, Saama Technologies and Tempus in all phases of clinical trials. Their goal is to streamline the procedure, cut down on expenses and improve how patients feel throughout the process. Many AI firms focus on forming alliances, merging with others and raising funds to grow their AI solutions and expand worldwide. Innovative AI tools for making predictions in real-time and monitoring are being introduced by startups, while companies with large infrastructure and data are adopting AI for scaling their work. Generally, the market is fast-changing and filled with competitors and regular innovations guide how the next generation of clinical trials should be done.

Artificial Intelligence Based Clinical Trials Market, Company Shares Analysis, 2024

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Recent Developments:

• In November 2024, Bioforum and Medidata have teamed up to provide AI solutions to handle data in clinical trials. AI is being introduced to help ensure accurate data and easy trial procedures, benefiting Bioforum’s clients in many parts of the world.
• In January 2024, Accenture has purchased stakes in QuantHealth, an Israeli company using AI for creating clinical trial designs. Having a platform in the cloud, QuantHealth allows companies to improve and speed up the development of new treatments.

Report Coverage:

By Offering

• Software
  • Clinical Trial Management Systems (CTMS)
  • Electronic Data Capture (EDC)
  • Predictive Analytics Tools
  • Patient Matching Platforms
• Services
  • AI-Powered Trial Design
  • Data Analysis & Interpretation
  • Patient Recruitment & Retention
  • Site Selection & Monitoring

By Process

• Trial Design
• Patient Selection
• Site Selection
• Patient Monitoring

By Clinical Trial Phase

• Phase I
• Phase II
• Phase III

By Therapeutic Application

• Oncology
• Cardiology
• Neurology
• Infectious Diseases
• Others

By End-User

• Pharmaceutical Companies
• Biotechnology Firms
• Contract Research Organizations (CROs)
• Academic & Research Institutes

By Region

North America

• U.S.
• Canada

Europe

• U.K.
• France
• Germany
• Italy
• Spain
• Rest of Europe

Asia Pacific

• China
• Japan
• India
• Australia
• South Korea
• Singapore
• Rest of Asia Pacific

Latin America

• Brazil
• Argentina
• Mexico
• Rest of Latin America

Middle East & Africa

• GCC Countries
• South Africa
• Rest of the Middle East & Africa

List of Companies:

• IBM Corporation
• Saama Technologies, LLC
• Deep 6 AI
• Unlearn.AI, Inc.
• Owkin, Inc.
• AiCure LLC
• Trials.ai, Inc.
• Antidote Technologies, Inc.
• BioSymetrics Inc.
• ConcertAI, LLC
• Innoplexus AG
• Intelligencia AI, Inc.
• Medidata Solutions
• Quanticate Ltd.
• Veristat, LLC

Frequently Asked Questions (FAQs)