Slow trial enrollment for brain tumors — AI patient-to-trial matching

Slow trial enrollment for brain tumors is a persistent bottleneck: neuro-oncology trials routinely miss recruitment targets, prolong sponsor timelines, and delay patient access to potentially life‑saving therapies. The burden falls on trial sponsors, CROs, academic cancer centers and, most critically, patients with aggressive or rare tumor subtypes who may be eligible for biomarker-driven cohorts but are not being identified efficiently. A focused AI patient-to-trial matching platform could ingest EHR notes, structured labs, genomics and imaging reports, apply advanced NLP and image-aware models to surface highly specific matches, and operationalize siteless enrollment workflows with human-in-the-loop confirmation and trial‑ops integration. The addressable market is large—an estimated $20.0B annually (20,000 trials/year × $1.0M average recruitment spend), with a market score of 92/100 and revenue potential of 88/100—so a solution that meaningfully shortens time-to-enroll has strong commercial upside. Current trends—decentralized, patient-centric trials, rapid improvements in AI/NLP on EHR data, and the move to precision oncology—make this window particularly attractive because sponsors increasingly prioritize remote, high‑precision matching. To differentiate you should be disease-focused (brain tumors), fuse multimodal data (imaging, pathology, genomics, clinical notes), secure federated partnerships with leading neuro‑oncology centers, and deliver transparent, prospectively validated scoring that demonstrably improves screening yield. The challenges are real—fragmented data access, variable biomarker testing rates, privacy/regulatory requirements and the need for prospective validation to earn sponsor trust—but with a realistic 12–24 month pilot plan and strong institutional partners this is a viable, high‑value opportunity worth pursuing if you can execute on data access and clinical validation.