Cleveland Clinic Accelerates Clinical Trial Recruitment with Roll Out of Dyania Health’s Artificial Intelligence Platform Across Health System

CLEVELAND & NEW YORK: Cleveland Clinic and Dyania Health today announced a collaboration to integrate Dyania Health’s Synapsis™ AI platform across Cleveland Clinic’s clinical research enterprise. This follows successful pilot programs in cardiology and oncology, as well as a unique effort where the teams jointly validated the technology’s effectiveness in neurology during an intensive testing period.

Together, the organizations aim to accelerate clinical trial recruitment by using medically trained large language models (LLMs) to speed up and scale patient identification for research studies, providing more patients faster access to potentially beneficial therapies.

Clinical trial recruitment remains one of the most persistent barriers to scientific progress. Approximately 80% of clinical trials do not meet enrollment timelines and 50% of trial sites fail to enroll any patients. By using artificial intelligence to automate chart review and rapidly identify eligible participants, Cleveland Clinic and Dyania Health are addressing these traditional enrollment bottlenecks and expanding patient access to potentially life-saving therapies.

“The future of medicine depends on building research systems that are precise, efficient, fair, and deeply connected to patient care,” said Lara Jehi, M.D., Chief Research Information Officer at Cleveland Clinic. “Through our innovative work with Dyania Health, we are creating an AI-driven foundation that helps identify the right patients for the right trials at the right time. This collaboration reflects our commitment to expanding access to clinical research and designing a research infrastructure that works for both investigators and the patients we serve.”

Among the studies run in the pilot program, a research team led by Aaron Gerds, M.D., M.S., Deputy Director for Clinical Research at Cleveland Clinic’s Cancer Institute, evaluated Synapsis AI alongside two experienced research nurses for a melanoma trial. On average, the technology identified an appropriate trial patient in 2.5 minutes with 96% accuracy, compared to 95% accuracy in 427 minutes by a melanoma-specialized nurse and 88% accuracy in 540 minutes by an oncology research nurse. These results, presented at the American Society of Clinical Oncology annual meeting, demonstrated the speed and scalability of the platform, and clinical-grade accuracy that complements the expertise of specialized healthcare professionals.

Similarly, among the studies conducted in collaboration with the Heart, Vascular & Thoracic Institute, one key highlight included the platform screening patients for the DepleTTR-CM trial, a Phase 3 study in transthyretin amyloid cardiomyopathy (ATTR-CM), a rare, progressive, potentially fatal disease of the heart muscle. The system analyzed more than 1.2 million patient records, reviewing 1,476 in one week and correctly identifying 30 eligible participants compared with 14 patients identified for screening over 90 days utilizing routine trial recruitment. It also generated understandable justifications for inclusion or exclusion criteria, a critical requirement for trial coordinators. Furthermore, the platform identified patients from a broader range of clinical sites within the health system, widening patient representation and community engagement, compared to typical recruitment processes that usually limit trials to the main hospital campus. Results were presented at the American College of Cardiology meeting by Trejeeve Martyn, M.D., Director of Heart Failure Population Health at Cleveland Clinic.

“Academic medical centers like Cleveland Clinic are home to some of the most advanced clinical research in the world, yet they often face significant challenges when trying to connect patients to trials – challenges rooted in complexity, time and fragmented data,” said Eirini Schlosser, founder and CEO of Dyania Health. “Through our collaboration with Cleveland Clinic, we are creating a new standard where AI enables faster connections between patients and potentially life-changing trials. This work is deeply personal. It is about ensuring that no opportunity for progress is lost in the complexity of data, and that innovation reaches people when it matters most.”

As part of the ongoing collaboration, Cleveland Clinic and Dyania Health are also validating and deploying new applications of the AI technology in neurology, including movement disorders and other conditions. The two teams annotated de-identified medical records to benchmark accuracy and improve patient identification for complex neurodegenerative conditions, with the goal of speeding up access to care and clinical trials.

Synapsis AI uses medically trained LLMs to abstract and interpret data such as clinical notes, medical records, imaging, pathology and combine it with other information such as organ function or age to draw accurate medical conclusions. The system integrates with electronic medical records and operates as a platform with tech-enabled workflows. By turning medical records into structured data that can be searched, Synapsis AI enables real-time, precision-matched patient identification based on complex, trial-specific eligibility criteria.

In addition to this collaboration, Cleveland Clinic has invested in Dyania Health and may benefit financially from the sale of this technology.