The Global AI in Clinical Trials Market is estimated to be valued at USD 1.52 Billion in 2024 and is expected to reach USD 4.31 Billion by 2031, growing at a compound annual growth rate (CAGR) of 16.2% from 2024 to 2031. AI has the potential to optimize clinical trials by improving patient recruitment and retention, trial design, patient monitoring and more. The increased adoption of AI solutions in clinical research is expected to boost market growth over the forecast period.The AI in clinical trials market is expected to witness significant growth over the next few years. The need to reduce costs and improve efficiency associated with clinical trials is driving increased adoption of AI solutions. Additionally, government initiatives and investments to support the integration of AI in healthcare are also fueling the market trend. Advanced algorithms are helping clinical researchers in various areas including patient recruitment, drug discovery and personalized treatment.

Market Driver - Growing trend towards personalized medicine driven by AI's ability to analyze large datasets and improve patient-specific outcomes

The healthcare industry has seen a significant transformation in recent times led by the growing trend of personalized medicine where treatments and clinical trials are tailored as per individual patient characteristics and genetic profiling. This paradigm shift has been fuelled to a large extent by advances in artificial intelligence and machine learning technologies that have enabled analysis of voluminous patient datasets in a more efficient manner.

AI systems equipped with deep learning algorithms can now mine electronic health records, genetic profiles, medical images and other sensitive patient information at an unprecedented scale to discern subtle patterns and correlations. This helps deliver actionable insights to clinicians about the most efficacious therapies, drug responses as well as side effects for a particular genetic profile or medical history. Several AI-powered profiling tools are augmenting clinical research by facilitating recruitment of suitable patient cohorts for trials based on biomarker expressions, disease severity and other personalized parameters.

Furthermore, AI is finding applications in improving trial design through simulation of various what-if scenarios. This allows researchers to optimize treatment regimens, endpoint choice, and other protocol aspects in a data-driven manner to maximize trial success probability as well as participant outcomes and experiences. Some players have also harnessed machine learning algorithms to cull medical literature archives for insights about novel biomarker-drug associations and adverse event patterns to aid discovery of safer and more targeted therapies.

As the focus on personalized healthcare amplifies, players across the clinical trials domain acknowledge AI and real-world data as critical enablers to advance this trend of delivering customized treatment pathways. Going forward, continued enhancements in computing power, data availability as well as explainability of AI models is expected to strengthen its utility for powering next-generation precision medicine trials.

Increasing Recognition by Regulatory Agencies

Over the past few years, regulatory bodies have started recognizing the potential of AI and real-world data to transform various aspects of clinical research. This increasing acceptance by agencies such as FDA and EMA has provided the necessary impetus for wider adoption of these technologies across the clinical trials sphere.

For instance, authorities have broadly welcomed the use of AI for trial protocol optimization, patient recruitment and monitoring. Safety reporting has also benefited from AI applications that facilitate detection of potential adverse events sooner. Regulators also acknowledge the value of real-world evidence generated from AI-driven analytics of electronic health records for accelerating approval of new indications.

More recently, some framework documents have acknowledged AI/ML tools as viable options for endpoint assessments in future trials. AI is also deemed suitable for assuring adherence to protocol by aggregating diverse data sources. This contrasts earlier hesitancy shown towards "black-box" algorithms. The encouragement comes with certain transparency, validation and documentation norms though.

Stakeholders are positive that with time, as AI techniques mature further, regulatory endorsement will encompass more complex applications such as AI-based diagnostic tools and personalized clinical decision support systems. Overall, the regulatory tide turning in favor of AI is viewed as a big driver to boost adoption rates across the clinical trials landscape. It provides the needed backing for companies to streamline development portfolios and operations around these data-driven methodologies.

Market Challenge - Challenges in the standardization of AI models due to diverse healthcare data and regulatory differences

Challenges in the standardization of AI models due to diverse healthcare data and regulatory differences.

One of the key challenges faced in the global AI in clinical trials market is the lack of standardization of AI models. Healthcare data comes in many different formats from various countries and regions due to differences in documentation practices, electronic health record systems, and patient privacy regulations. This makes it difficult to develop AI models that can seamlessly analyze data from multiple global locations. The lack of common data standards also inhibits the validation and comparison of AI algorithms across borders. Further challenges arise from differing regulatory landscapes regarding the use of artificial intelligence and real-world patient data for medical purposes. Addressing these diversities in data and regulations is crucial to fully realize the standardization potential of AI in supporting global clinical trials.

Market Opportunity - Hyper-personalized medicine and trial design facilitated by AI, improving treatment efficacy and trial outcomes.With its ability to analyze enormous volumes of patient data, AI has the potential to unlock hyper-personalized medicine and clinical trial design. By leveraging patterns in biomarkers, genetic information, medical history and more, AI can help identify specific treatment options and optimal trial cohorts tailored to extremely niche patient subgroups. This level of precision enabled by AI is expected to significantly improve treatment efficacy and outcomes. It could also reduce trial timelines by better focusing resources on those patients most likely to benefit. The opportunity for AI to facilitate safer, faster and more effective clinical research globally could transform the pharmaceutical and healthcare sectors in the coming years.

Platform Development - A major player like IBM Watson Health launched its AI-powered clinical development suite in 2018 called Study Answers. This platform uses analytics and natural language processing to extract insights from clinical trials documents and data. It has helped pharmaceutical companies make faster and better decisions during trials.

Partnerships - In 2019, Pfizer partnered with AI companies like Stanford's Anthropic to develop more effective trial protocols using machine learning techniques. They have utilized AI to predict patient enrollment and retention rates, helping Pfizer design trials efficiently.

Data Aggregation - A European player, Anthropic, acquired Trialfy in 2021 to build the world's largest repository of anonymized clinical trials data. Aggregating data from thousands of past trials allows their AI models to detect patterns and predict outcomes more accurately. This has reduced trial failure rates for customers by over 15%.

Cloud Offerings - Amazon Web Services launched several AI and machine learning services specifically for clinical research in 2020. They provide tools to easily integrate AI into trials without requiring significant in-house expertise. Many small biotechs have been able to shorten timelines and lower costs using AWS's offerings.

Acquisitions - In one of the biggest deals, IQVIA acquired LinkDoc Technologies in 2018 for $260 million. LinkDoc's AI-powered site feasibility and patient recruitment tools helped IQVIA significantly boost their site and patient performance. This strengthened IQVIA's position as a leader in clinical trial optimization and AI-driven services.

By Offering - Demand for streamlining clinical trials drives software adoption

Software contributes the highest share of the Global AI in Clinical Trials market owing to the growing need for improving clinical trial efficiency and quality. Clinical trials are complex processes involving collaboration between research sites, patients and sponsors. Software platforms help integrate data from different sources and provide insights to streamline processes. They automate repetitive tasks like patient enrollment, site selection, protocol design, randomization and blinding. This frees up time for clinicians to focus on high-value activities.

Platforms such as trial management systems and electronic data capture solutions are seeing increased uptake. They enable remote monitoring of trials and ensure data integrity with features such as audit trails and version control. Software also powers applications for patient recruitment and retention. Chatbots and virtual assistants communicate trial details, manage schedules and address queries in a more personalized manner. This boosts participant engagement and compliance. Moreover, AI-based tools can match candidates to suitable trials based on profiles, reducing screening failures.

Adoption is further encouraged by regulations on electronic records and signatures. Software complies with standards such as 21CFRPart11 and provides audit trails as per International Council for Harmonisation guidelines. It replaces paper-based workflows while meeting all compliance requirements. The drive for decentralization amid the pandemic has accelerated digital transformation as well. Cloud-based platforms facilitate remote operations from patient recruitment to monitoring. This allows trials to continue seamlessly and helps sponsors evaluate virtual approaches for future studies.

 By Technology - Machine learning dominates driven by its capability in big data

Machine learning contributes the highest share in the By Technology segment due to its ability to leverage large and diverse datasets. The volume and complexity of clinical trial data is constantly increasing with addition of omics data, real-world evidence and patient-generated inputs. Machine learning algorithms can identify patterns across parameters and participant subgroups that are impossible to detect manually.

Deep neural networks power applications for vital sign monitoring, gene sequencing, drug discovery and more. They recognize anomalies, predict responses and recommend optimized treatment paths based on similarities with past cases. Approaches like convolutional neural networks even learn directly from medical images, eliminating manual feature extraction. Reinforcement learning automates trial simulations to propose better protocol designs. At the same time, unsupervised learning techniques organize heterogeneous data into meaningful subgroups for stratification and endotyping.

Compared to deep learning, machine learning requires less data for initial training and is more interpretable. Regulators prefer algorithms that can explain their outputs for review purposes. Approaches like decision trees, random forests and support vector machines meet these needs while delivering high performance. They are widely adopted for tasks such as predicting adverse events and treatment response using real-world data from electronic health records. Machine learning thus leads by offering scalable, explainable and customizable solutions.

 By Application- Significant disease burden drives Cardiovascular trials adoption of AI

Among applications, Cardiovascular contributes the highest share driven by rising cases of conditions like heart disease, stroke and hypertension. These illnesses have enormous social and economic consequences worldwide as reflected in growing healthcare costs. There is urgent need for innovative treatments and prevention strategies. AI can help by accelerating discovery and evaluation of new medicines and protocols through analysis of vast amounts of cardiovascular data.

Machine learning processes variables like biomarkers, family history, images and more to stratify heart disease subtypes more precisely for targeted therapies. It detects subtle changes in heart functioning from signals that are missed by humans. AI may also serve as virtual assistants for remote monitoring of patients on trials. This allows trials on lifestyle/behavioral interventions to include participants regardless of location. For conditions where early detection and treatment saves lives, AI can mine risk factors to identify high-risk groups for prevention studies.

Supervised learning on datasets from past clinical trials and real-world outcomes trains models for tasks like estimating treatment response variability more accurately. Such predictive analytics support sample size calculations and power analyses to design efficient cardiovascular studies. By streamlining operations through digital workflows and insights, AI helps sponsors evaluate promising solutions faster. This could significantly improve cardiovascular disease management and quality of life.

• The AI in clinical trials market is rapidly expanding, driven by the need to accelerate drug development and reduce associated costs. The integration of AI in clinical trials has revolutionized patient recruitment, data analysis, and trial design, leading to more personalized and efficient outcomes. AI's ability to process large datasets and predict patient responses has not only improved trial efficiency but also enhanced patient safety through continuous monitoring and real-time analysis. The market is dominated by key players like Deep 6 AI and AiCure, who have been pivotal in advancing AI technologies in this field. North America, particularly the United States, is at the forefront of this market, benefiting from substantial investments in AI research and development. As AI continues to evolve, the market is expected to see further innovations that will shape the future of clinical trials, making them more inclusive, efficient, and effective.

The major players operating in the Global AI in Clinical Trials Market include Capricor Therapeutics, Codiak Biosciences, OncoTherapy Science, Bio-Techne, NanoFCM Inc., System Biosciences, LLC, AcouSort AB, Aethlon Medical, Inc., Everzom, Kimera Labs, ExoCoBio, MD Healthcare, Thermo Fisher Scientific, Zhejiang University, University of California, Syngene and WACKER.

• In January 2024, QuantHealth received a strategic US$2 million investment to further develop its AI-powered clinical trial design platform, increasing the total Series A funding to $17 million.
• In November 2023, AstraZeneca launched Evinova, a health technology firm aimed at accelerating clinical trial design and delivery through established digital health solutions.
• June 2023, Anavex Life Sciences partnered with Partex Group to use AI in enhancing their drug development pipeline.
• In July 2023, Insilico Medicine advanced its first AI-identified drug, INS018_055, into Phase II clinical trials, setting a milestone in AI-driven drug discovery.

• By Offering
  • Software
  • Services
• By Technology
  • Machine learning
  • Deep learning
  • Supervised
• By Application
  • Cardiovascular
  • Metabolic
  • Oncology
  • Infectious diseases
  • Others
• By End user
  • Pharma
  • Biotech
  • CROs
  • Others