Escalating Cancer Burden

Over 2 million new US cancer cases are projected for 2025, equating to approximately 1,700 deaths daily. This growing crisis strains healthcare systems and intensifies the need for efficient access to innovative trial therapies. (Source: American Cancer Society, 2025 ¹)

The Precision Medicine Paradox

The oncology clinical trials market is expanding rapidly, set to reach USD 22.76 billion by 2034 (5.28% CAGR).² However, the crucial shift towards biomarker-driven trials (e.g., biomarker testing for NSCLC rose from 55.3% in 2011 to 88.1% in 2021 ³) creates highly specific patient criteria, fragmenting patient pools and making manual matching increasingly complex and inefficient.

Persistent Enrollment Crisis

Only 2-4% of adult cancer patients participate in clinical trials.⁴ Consequently, an estimated 20% of trials fail due to insufficient enrollment ⁴, a problem exacerbated by manual inefficiencies that overlook up to 70% of eligible patients ⁵, critically delaying the availability of new treatments.

Staggering Economic Drain

Inefficient trial matching and subsequent failures contribute to an estimated USD 50-60 billion spent annually on unsuccessful oncology trials.⁶ Furthermore, operational delays in active trials can cost between USD 600,000 and USD 8 million per month ⁷, diverting vital resources from further innovation.

The AI Inflection Point is Now

Artificial Intelligence can slash trial matching time by 99.9% (e.g., 1 AI hour versus 19,500 manual hours) and more than double the identification of potential patient matches.⁸ AI's advanced capability to process complex biomarker data ⁹ offers an immediate, transformative solution to connect the right patient to the right trial, faster than ever before.

Surge in AI-Driven Therapies

AI is dramatically accelerating drug discovery, leading to a significant increase in novel therapies entering the clinical trial system. In 2023, 46 AI-discovered drugs reached phase II and III clinical trials.¹⁰ The overall scale of new therapies entering development is substantial; for example, the U.S. FDA receives an average of approximately 1,500 Investigational New Drug (IND) applications annually, each representing a potential new therapy beginning its journey through clinical trials.¹¹ This large influx of promising treatments, with AI contributing to this pipeline's growth, underscores the urgent need for efficient patient matching to ensure these therapies can reach the market and benefit patients.