Gilead Sciences has partnered with Tempus AI to gain expanded access to the company's real-world data resources and AI platform capabilities, according to GenomeWeb. The agreement broadens Gilead's use of Tempus's multimodal clinical and molecular datasets, which the Chicago-based firm has built from its network of health system partnerships and clinical sequencing operations.
Financial terms were not disclosed. Tempus, which trades on Nasdaq under the ticker TEM, has positioned its data licensing and AI analytics offerings as a core revenue stream alongside its clinical genomics laboratory business. The Gilead deal adds another large-cap pharma name to Tempus's roster of biopharma customers paying for access to deidentified clinical records, genomic sequencing results, and AI-driven analytics tools. Gilead's oncology portfolio gives it commercial motivation to mine real-world evidence for treatment optimization, biomarker discovery, and clinical trial design.
Separately, SoftBank Group has launched an AI healthcare joint venture with Tempus AI, according to Reuters, signaling investor confidence in the company's platform beyond its pharma data licensing business.
Across the precision medicine sector, partnerships linking data platforms to diagnostic and therapeutic companies have intensified. Qiagen and Sophia Genetics recently announced a collaboration pairing Qiagen's NGS sample-prep reagents with Sophia's AI-driven analytics platform. Illumina struck co-commercialization deals with Labcorp and Veritas Genetics to distribute sequencing tests through hospitals and insurers. And GRAIL integrated its Galleri multi-cancer early detection test into Epic's electronic health record platform, gaining access to more than 300 million patient records.
For Tempus, the Gilead deal reinforces its data monetization model, which differentiates it from diagnostic-focused competitors that derive revenue primarily from test reimbursement. The company's ability to maintain and expand data-sharing agreements with health systems under evolving privacy frameworks will determine whether the data-as-a-service model scales or plateaus.