MyTomorrows and CUN Partner on AI-Assisted Patient Trial Matching

Why It Matters

Automating eligibility checks accelerates patient enrollment, cuts administrative burden, and improves trial success rates, reshaping how hospitals connect patients to innovative therapies.

Key Takeaways

• AI matches patients to trials within CUN’s EHR
• Large language models analyze multilingual records and molecular data
• Real-time cohort status reduces missed eligibility
• GDPR‑compliant platform streamlines referrals to external centers
• First myTomorrows extensive site partnership in Spain

Pulse Analysis

Artificial intelligence is rapidly becoming a catalyst for solving one of clinical research’s longest‑standing bottlenecks: patient recruitment. Traditional trial matching relies on manual chart reviews, often missing eligible candidates hidden in free‑text notes or complex molecular reports. By deploying large language models that understand multiple languages and can interpret both narrative and structured data, myTomorrows offers a scalable solution that standardizes eligibility assessment across diverse oncology settings. This technology not only shortens the time from diagnosis to trial consideration but also improves the precision of matches, potentially raising overall enrollment rates.

The integration with CUN’s electronic health record illustrates how AI can be woven into existing clinical workflows without disrupting care delivery. Once a physician opens a patient’s chart, the AI engine instantly generates a curated list of open studies, reflecting current cohort availability and protocol nuances. Because the platform operates within a GDPR‑compliant environment, patient privacy remains protected while enabling seamless referral coordination with external institutions via a branded CUN website. This real‑time visibility reduces back‑and‑forth communications, allowing referring doctors to pre‑screen patients and submit referrals that align with trial requirements from the outset.

For the broader European market, the partnership signals a shift toward data‑driven trial ecosystems that can be replicated across hospitals and research centers. myTomorrows’ strategy of establishing deep site collaborations, starting with CUN, positions it to capture a growing demand for efficient trial enrollment tools amid rising regulatory scrutiny and competitive pressure from pharma sponsors. As more institutions adopt AI‑enabled matching, the industry may see faster drug development timelines, improved patient outcomes, and a new standard for integrating clinical research into everyday patient care.