Global AI-Based Clinical Trials Solution Market Growth Analysis, Opportunities, Trends, Developments and Forecast Report, 2030

According to the latest market research study published by P&S Intelligence, the global AI-based clinical trials solution market is witnessing exponential growth, driven by technological innovation and the pressing need for more efficient drug development pathways. Valued at USD 2.4 billion in 2024, the market is poised to reach USD 5.4 billion by 2030, expanding at a robust CAGR of 14.5% during 2025-2030. This surge is primarily attributed to the growing prevalence of chronic diseases and rising investments in advanced drug development.

Artificial intelligence is revolutionizing clinical trials by streamlining processes such as trial design, site selection, patient recruitment, and ongoing monitoring. As pharmaceutical companies seek to replenish their drug pipelines with more precision and cost-efficiency, AI-based solutions are rapidly gaining traction. Public and private sector initiatives are further accelerating the adoption of these technologies, aiming to shorten drug development timelines and improve trial outcomes.

Additionally, the outsourcing of trial activities to contract research organizations (CROs), known for their cost-efficiency, is expected to significantly boost the market. Countries such as the U.S., Japan, and France are leading in healthcare research, while the U.K. is focusing on R&D to provide cutting-edge technologies globally.

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Key Insights

• The clinical trial phase segment is led by Phase II, which accounted for 40% of the revenue in 2024, due to the high number of active validation studies and the integration of smart data analysis technologies.

• Phase I is expected to witness the fastest growth, as AI improves patient recruitment, trial design, and predictive modeling for earlier and more effective outcomes.

• The oncology category dominates the therapeutic application segment with a 30% market share in 2024, growing at the highest CAGR of 15.0% through 2030, driven by the high volume of oncology trials and urgent need for new treatments.

• Other key therapeutic areas include cardiovascular, neurological, metabolic, and infectious diseases, all benefiting from AI's ability to streamline study design and monitor outcomes more effectively.

• Pharmaceutical companies remain the largest end-user group, leveraging AI to optimize trial efficiency, increase success rates, and reduce time to market for new drugs.

• Academia is also increasingly adopting AI technologies to enhance research capabilities and clinical study design.

• North America is the largest regional market, owing to strong R&D investment, advanced healthcare infrastructure, and supportive regulatory frameworks.

• Asia-Pacific is set to be the fastest-growing region, with countries such as China, India, and Japan investing heavily in digital health and AI-driven research platforms.

• Real-world evidence (RWE) is emerging as a transformative trend, involving data collected from electronic health records, wearables, social media, and patient registries to enhance patient-centricity and improve trial recruitment and design.

• Agencies like the FDA and EMA are increasingly recognizing the value of RWE, encouraging broader integration of non-traditional data sources into clinical research.

• Technological partnerships are accelerating innovation; for example, the collaboration between Exscientia and Sumitomo Dainippon Pharma led to the discovery of DSP-1181 in under 12 months-substantially faster than the traditional average of 4.5 years.

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• AI-based platforms can cut trial costs by more than 30%, providing significant ROI to stakeholders while improving safety and accuracy in drug development.

• Challenges remain around ensuring data quality and integrity, especially when integrating data from diverse sources like wearables and electronic health records.

• Harmonizing and standardizing this data is resource-intensive but crucial to maintaining accuracy and regulatory compliance.

• The market remains fragmented, offering opportunities for new entrants and niche players to develop innovative AI-driven tools for clinical trials.

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This release was published on openPR.