Segmed Partners with Verily to Expand Access to Real-World Imaging Data

Why It Matters

By embedding massive, multimodal imaging data into a secure, self‑serve environment, the deal speeds AI‑driven precision‑health research and accelerates diagnostic and therapeutic innovation.

Key Takeaways

• Segmed adds 150M de-identified imaging exams
• First dataset focuses on longitudinal breast cancer DBT
• Data integrated into Verily’s Pre Exchange platform
• Researchers gain secure, self‑serve AI‑native access
• Partnership accelerates precision‑health diagnostics development

Pulse Analysis

Real‑world imaging data has become a cornerstone for training robust AI models in healthcare, yet access to large, high‑quality, privacy‑compliant datasets remains a bottleneck. Segmed’s repository, encompassing roughly 150 million de‑identified exams across modalities such as CT, MRI, PET and mammography, offers a rare breadth of clinical nuance. By curating a longitudinal breast‑cancer cohort with digital breast tomosynthesis and confirmed pathology, Segmed provides researchers a ready‑to‑use asset that mirrors the heterogeneity seen in everyday practice, thereby improving model generalizability and reducing bias.

Verily’s Pre Exchange and Workbench environments are purpose‑built for secure, self‑serve analytics, combining AI‑native tooling with stringent data governance. Integrating Segmed’s datasets into this ecosystem eliminates the need for cumbersome data‑transfer agreements and allows investigators to query multimodal imaging alongside associated clinical variables within a trusted research enclave. This streamlined workflow not only cuts time‑to‑insight but also democratizes access for academic labs and biotech firms that lack extensive data‑engineering resources, fostering a more collaborative innovation landscape.

The partnership signals a broader shift toward open, yet privacy‑first, data ecosystems that underpin precision‑health initiatives. As AI models become integral to early cancer detection, treatment planning, and drug development, the availability of large, annotated imaging cohorts will accelerate validation pipelines and regulatory acceptance. Moreover, the collaboration showcases how strategic alliances between data custodians and platform providers can create scalable, secure avenues for scientific discovery, ultimately driving faster translation of AI breakthroughs into clinical practice.