AI in Clinical Trials Market: Growth, Size, Share, and Trends

Based on offering, the end-to-end solutions segment holds the largest share of the AI in clinical trials market in 2023. The growth of this segment is attributed to the rising demand for streamlined, unified platforms that integrate study design, site selection, patient recruitment, and real-time data analytics in a single workflow. End-to-end solutions reduce operational complexity, minimize errors associated with multiple disconnected systems, and accelerate trial timelines. Their ability to offer greater scalability, enhanced compliance, and improved data transparency makes them increasingly preferred by pharmaceutical companies, biotech firms, and CROs seeking more efficient and cost-effective trial management.

In 2023, the patient recruitment segment accounted for the largest share of the AI in clinical trials market. This dominance is driven by the increasing use of AI to identify eligible participants more accurately and reduce recruitment timelines, which remain one of the most significant bottlenecks in clinical research. AI-powered tools analyze electronic health records, genetic data, and real-world evidence to match patients with trial criteria more efficiently. These capabilities help improve enrollment rates, lower trial costs, and enhance the overall success of clinical studies, leading to strong adoption across pharmaceutical companies and CROs.

In 2023, the phase II clinical trials segment accounted for the largest share of the AI in clinical trials market. This is largely due to the growing complexity of mid-stage trials, where determining optimal dosing, assessing efficacy, and identifying patient subgroups require advanced analytical support. AI tools enhance decision-making by predicting treatment responses, improving protocol design, and enabling more precise patient stratification. As phase II outcomes heavily influence go/no-go decisions for later-stage development, the use of AI to reduce risks, shorten timelines, and improve data quality has driven greater adoption in this segment.

By deployment mode, the cloud-based solutions segment will grow the fastest during the forecast period. This rapid growth is driven by the need for scalable, flexible, and cost-efficient platforms that can manage large and complex clinical trial datasets. Cloud solutions support real-time data access, remote monitoring, and easier integration with decentralized trial technologies. With faster deployment, enhanced security, and improved collaboration across global teams, cloud-based platforms are becoming increasingly essential for accelerating AI adoption in clinical research.

By indication, the oncology segment is expected to dominate the market, growing at the highest CAGR. This growth is driven by the rising global burden of cancer and the increasing demand for advanced tools to manage complex oncology trial designs. AI enables faster identification of biomarkers, improved patient stratification, and more accurate prediction of treatment responses. With a surge in targeted therapies, immunotherapies, and personalized medicines, the need for AI-powered analytics continues to expand. As a result, oncology trials are adopting AI at a faster pace to enhance efficiency, reduce timelines, and improve clinical outcomes.

By technology, the machine learning segment is expected to dominate the market. This dominance is driven by the growing use of machine learning algorithms to analyze large, complex clinical datasets and generate predictive insights that enhance trial design and execution. ML supports faster patient identification, improved endpoint prediction, and real-time anomaly detection, helping reduce delays and operational risks. As biopharmaceutical companies increasingly rely on data-driven decision-making, machine learning has become essential for improving accuracy, efficiency, and overall trial success rates.

By application, the biomarkers segment is expected to dominate the market. The segment leads the market due to the growing emphasis on precision medicine and the need to identify targeted patient populations more effectively. AI accelerates biomarker discovery by analyzing genetic, proteomic, and clinical data to uncover patterns that support better patient stratification and treatment response prediction. As oncology, rare diseases, and personalized therapies expand, biomarker-driven trials increasingly rely on AI to improve accuracy, shorten development timelines, and boost clinical success rates.

The pharmaceutical and biopharmaceutical companies segment accounted for the largest share in 2023, supported by their growing reliance on AI to streamline drug development and reduce trial timelines. These organizations handle large, complex datasets and increasingly adopt AI for patient recruitment, protocol optimization, and real-time monitoring. With rising R&D investments and a strong focus on precision medicine, pharma and biotech firms are integrating AI across multiple trial phases to enhance efficiency, lower operational costs, and improve the likelihood of successful outcomes.