AI in Clinical Trial Market To Reach USD 22.3 Billion by 2033

Trishita Deb
Trishita Deb

Updated · Mar 27, 2024

SHARE:

At Market.us Media, we strive to bring you the most accurate and up-to-date information by utilizing a variety of resources, including paid and free sources, primary research, and phone interviews. Learn more.
close
Advertiser Disclosure

At Market.us Media, we strive to bring you the most accurate and up-to-date information by utilizing a variety of resources, including paid and free sources, primary research, and phone interviews. Our data is available to the public free of charge, and we encourage you to use it to inform your personal or business decisions. If you choose to republish our data on your own website, we simply ask that you provide a proper citation or link back to the respective page on Market.us Media. We appreciate your support and look forward to continuing to provide valuable insights for our audience.

Introduction

The AI in Clinical Trial Market is experiencing significant growth due to the rapid advancement and integration of artificial intelligence technologies within the clinical trials industry. In 2023, the market was worth USD 1.8 billion and is forecasted to reach USD 22.3 billion by 2033, with a CAGR of 28.6%. This growth is due to the increasing demand for innovative and efficient trial designs, especially with rising healthcare costs and the need for accelerated drug development timelines.

The market’s expansion is driven by several key factors. First, there’s a growing need to reduce the costs associated with clinical trials and shorten the time involved in the drug development process. This need aligns with the broader adoption of cloud-based applications and services in the industry. Additionally, AI’s role in enhancing the productivity and efficacy of clinical trials is crucial, as it improves decision-making processes at every stage – from asset and portfolio strategy to protocol and trial design. AI is used to identify promising indications for novel assets, refine trial eligibility criteria, optimize trial endpoints, and support portfolio strategy decisions, ultimately contributing to a more targeted and efficient clinical trial process.

However, the market does face some challenges, including the shortage of skilled AI workforce and the stringent regulatory guidelines that govern medical software. Despite these hurdles, the market is ripe with opportunities, particularly in the development of novel clinical trial designs for complex therapies and the burgeoning drug and biologics market. The integration of AI in clinical trials is proving instrumental in overcoming traditional limitations, such as the limited availability of datasets that hinder the scope and scale of clinical research.

Recent developments in the integration of artificial intelligence (AI) in clinical trials have highlighted significant advancements, notably through funding rounds and mergers and acquisitions (M&As) that underscore the sector’s growth and innovation. Unlearn.AI, a pioneering firm specializing in the development of digital twins for clinical trials, secured a substantial $50 million in a Series B funding round. This infusion of capital is aimed at enhancing the company’s capabilities in creating digital replicas of patients to facilitate smaller, more efficient studies, leveraging AI and historical data. Unlearn.AI’s approach represents a shift towards more nuanced and predictive modeling in clinical trial design, potentially reducing the duration and cost of trials.

Moreover, the clinical research organization (CRO) sector has witnessed a record number of M&As, with 50 completed deals being reported. This surge in M&A activity is indicative of a broader trend towards consolidation in the industry, as companies strive to expand their capabilities and adapt to the evolving landscape of clinical trials. The increase in M&A activity also reflects a strategic push to leverage new technologies and methodologies, including AI, to enhance the efficiency and effectiveness of clinical research.

These developments signify a growing recognition of the value of AI and digital technologies in transforming clinical trials, from patient recruitment and data analysis to trial monitoring and outcomes assessment. As investment continues to flow into AI applications for clinical trials, the potential for these technologies to streamline processes, reduce costs, and accelerate the development of new therapies becomes increasingly apparent.

Key Takeaways

  • The AI In Clinical Trial Market was valued at USD 1.8 billion in 2023 and is forecasted to reach USD 22.3 billion by 2033, with a remarkable CAGR of 28.6%.
  • Services segment dominated the market with a substantial market share of 61.3% in 2023, showcasing the importance of tailored solutions in meeting diverse clinical trial needs.
  • Deep learning technology held a significant market share of 54.7% in 2023, highlighting its superiority in processing complex clinical trial data and improving diagnostics and patient stratification.
  • The oncology application segment commanded a dominant market share of 45.9% in 2023, driven by the increasing prevalence of cancer globally and the development of AI tools tailored for oncology clinical trials.
  • Pharmaceutical companies emerged as the leading end users, capturing a substantial market share of 65.8% in 2023, as AI facilitates efficient data analysis, accelerates drug development, and improves clinical trial outcomes.
  • North America led the market with a notable market share of 31.5% in 2023, attributed to the presence of major market players, high adoption of AI technologies, and significant investments in healthcare innovation.
  • The AI in Clinical Trial Market is anticipated to witness the fastest growth rate of 31.5% in the Asia-Pacific region, driven by increasing AI adoption, favorable government initiatives, and rising clinical trial enrollments.
  • The market is characterized by strategic partnerships, product innovations, and mergers & acquisitions among key players such as Phesi, Intelligencia, DEEP LENS AI, and others.
To learn more about this report – request a sample report PDF

AI in Clinical Trial Statistics

  • Machine learning (ML) is used to find promising drug molecules and the right patients for clinical trials, leading to faster and smarter trial design.
  • AI has improved how we choose trial sites and find patients, resulting in a 20.6% increase in patient enrollment in cancer studies.
  • AI is making it easier to handle the huge amount of data in pharmacovigilance, with tools processing around 800,000 cases a year and automating 70% of the work.
  • Clinical trial monitoring is more efficient with AI, reducing the time needed to solve queries by 37%, cutting down on data checking work by 31%, and speeding up trial close-out by 13 days.
  • In patient care, AI helps find patients who might develop certain diseases like Alzheimer’s, with a success rate of 79% in identifying them correctly, much better than older methods.
  • The journey from starting a clinical trial to getting a drug approved takes about 10 to 12 years, with only 12% of drugs making it through.
  • Pharma companies use AI to try to improve these odds, aiming for better success rates in FDA approvals.
  • The FDA has started to outline how AI and ML might be used in drug development as of May 2023, looking at how to keep the technology in check and ensure the data it uses is good quality.
  • A Phase III clinical trial can generate over 3.56 million pieces of data, showing just how much information needs to be managed.
  • Medical data is expected to grow by up to 5 times each year, highlighting the increasing role of AI in managing and making sense of this information.

Use Cases

Artificial Intelligence (AI) is significantly reshaping clinical trials, offering diverse applications that enhance efficiency, accuracy, and patient outcomes. Here are several key use cases illustrating AI’s transformative potential in clinical trials:

  • Indication Selection for Asset Strategy: AI, combined with Real-World Data (RWD), aids in identifying promising indications for both existing and novel assets. This process involves analyzing patient outcomes under various conditions and leveraging data to expand or shift indication strategies effectively.
  • Optimizing Protocol Design: AI’s role begins at the foundational stages of clinical trials by improving study protocols through historical data analysis. This leads to scientifically robust trials with efficient and patient-centric designs, significantly reducing amendments and enhancing the likelihood of success.
  • Patient Recruitment: AI analyses patient data, EHRs, and medical literature to identify candidates who meet specific trial criteria, considering factors like location and demographic profiles. This method speeds up the recruitment process and ensures a more precise selection, addressing challenges such as patient accessibility to trial sites.
  • Real-Time Safety Monitoring: Through continuous data analysis, AI identifies potential safety issues and adverse events in real-time, enabling prompt actions to safeguard patient safety and ensure regulatory compliance.
  • Digital Twin Models: AI creates virtual replicas of patients based on genetic, medical history, and ongoing health data. These models simulate and predict outcomes, advancing personalized and safer healthcare solutions.
  • Treatment Response Prediction: AI develops predictive models to assess patient responses to different treatments, optimizing efficiency and reducing risks. This approach is pivotal in personalizing medicine and improving patient outcomes.

Leveraging AI in clinical trials can improve treatment time-to-market, cost efficiency, regulatory compliance, enhanced data analysis and management, personalized medicine, and patient outcomes. AI reduces manual labor and repetitive tasks, allowing for a more streamlined and efficient drug development process.

Recent Developments

  • Novartis and Chinook Therapeutics Deal: Novartis AG announced its plan to acquire Chinook Therapeutics for up to $3.5 billion. This move is part of Novartis’s strategy to focus on the renal therapeutics market, addressing the rising prevalence of chronic kidney disease.
  • Roche’s Acquisition of Carmot Therapeutics: Roche entered into a definitive merger agreement with Carmot Therapeutics, planning to acquire the company and its clinical-stage obesity drugs portfolio for $2.7 billion, with an additional $400 million in potential milestone payments.
  • Abbott’s Acquisition Plans for Bigfoot Biomedical: Abbott announced plans to acquire Bigfoot Biomedical in the third quarter of 2023, aiming to enhance its diabetes management portfolio.
  • Eli Lilly to Acquire Dice Therapeutics: Eli Lilly disclosed plans to acquire Dice Therapeutics for $2.4 billion in cash, focusing on expanding its immunology capabilities.
  • GSK’s Acquisition of Bellus Health: GSK reached an agreement to acquire Bellus Health Inc. for nearly $2 billion, aiming to access new treatments for respiratory diseases.
  • Sanofi and Provention Bio Deal: Sanofi announced a $2.9 billion acquisition deal with US-based Provention Bio, aiming to enhance its portfolio in the healthcare sector.
  • Pfizer’s Consideration for Seagen Acquisition: Pfizer is reportedly in talks to acquire cancer drugmaker Seagen Inc, following a previously considered acquisition by Merck that did not proceed.
  • Envision Pharma Group Acquires OKRA.ai: Envision Pharma Group announced the acquisition of OKRA.ai, a leader in AI solutions for pharmaceutical operations. This acquisition aims to enhance Envision’s capabilities in delivering AI-driven insights for the pharmaceutical industry.
  • AstraZeneca Buys CinCor Pharma: AstraZeneca announced its acquisition of CinCor Pharma in a deal valued at $1.8 billion, aiming to accelerate its development in treating cardiovascular diseases.
  • Moderna’s First-Ever Acquisition of OriCiro: Moderna Inc acquired Japan-based OriCiro Genomics K.K. for $85 million, marking its first-ever acquisition. This deal aims to bolster Moderna’s biopharmaceutical applications.

Conclusion

These developments signify a growing recognition of the value of AI and digital technologies in transforming clinical trials, from patient recruitment and data analysis to trial monitoring and outcomes assessment. As investment continues to flow into AI applications for clinical trials, the potential for these technologies to streamline processes, reduce costs, and accelerate the development of new therapies becomes increasingly apparent.

SHARE:
Trishita Deb

Trishita Deb

Trishita has more than 7 years of experience in market research and consulting industry. She has worked in various domains including healthcare, consumer goods, and materials. Her expertise lies majorly in healthcare and has worked on more than 400 healthcare reports throughout her career.