Tempus Lens — Next Generation is an agentic AI environment for oncology research, released by Tempus AI on May 31, 2026. It is designed to convert biological hypotheses or research questions entered in natural language into actionable, customized research plans, enabling AI agents to execute code on multimodal oncology data and generate results. Rather than functioning as a chatbot that simply answers questions with text, it serves more like a digital research partner that bridges research inquiries through data definition, analysis procedures, and result interpretation. Just as GPT processes natural language in terms of tokens and context, Lens aims to translate oncology questions into cohort conditions, clinical variables, molecular biomarkers, and analytical tasks. In traditional oncology data research, clinicians propose hypotheses that data scientists must translate into cohort definitions and code, clean disparate clinical and molecular datasets, and then interpret statistical results back into research language. This process requires iterative cross-functional communication and manual verification, often resulting in significant delays even when analyzable data is available. The key differentiator of Lens lies in its integration of large-scale real-world oncology data, domain models, validated AI agents, and scientific workflows into a single drug development environment. While general-purpose generative AI may only suggest analytical approaches, Lens pursues an execution-centric architecture where agents run code on relevant data and produce results along with supporting evidence packages. From the perspective of biotech researchers, core use cases include clinical trial cohort design, biomarker discovery, and the curation of evidence for decision-making. For example, a researcher can submit a natural language query containing specific cancer types, treatment history, molecular variants, and clinical outcomes; Lens then concretizes this into inclusion/exclusion criteria for an analyzable patient cohort and a detailed research plan. The generated results can be used to characterize candidate patient populations or pre-evaluate trial design hypotheses, but researchers must directly verify critical factors such as exact cohort size, statistical models, missing data handling, and confounding variable adjustment conditions. Detailed parameters and supported statistical methods published in official materials require further confirmation. In biomarker research, Lens can be used to jointly examine clinical phenotypes and tumor molecular information to explore associations between specific variants or biological signals and treatment responses, thereby constructing evidence for follow-up experiments or validation studies. Additionally, during candidate compound or indication evaluation, it can consolidate clinical trial cohort definitions, exploratory findings, and related evidence into a single package to facilitate cross-team review. However, associations identified in observational data do not automatically establish causality or clinical validity; they must be followed by external validation cohorts, pre-defined statistical analysis plans, and review by medical and regulatory experts. Data access scope, export policies, result reproducibility, and audit trail capabilities should also be verified prior to actual implementation.

Tempus Lens — Next Generation is an agentic AI environment for oncology research, released by Tempus AI on May 31, 2026. When researchers input biological hypotheses or research questions in natural language, the system converts them into customizable, analyzable research plans and enables AI agents to execute code on multimodal oncology data to generate results. Rather than functioning as a chatbot that simply answers questions with sentences, it serves more like a digital research partner that guides users from research questions through data definition, analysis procedures, and result interpretation. Just as GPT processes natural language at the token and context levels, Lens handles oncology

🔬 Precision Oncology Clinical Trial Cohort Design: When a researcher provides natural language inputs specifying cancer type, treatment history, molecular variants, and clinical outcome criteria, Lens converts them into analyzable inclusion/exclusion criteria and a study plan. Patient sample size calculations, follow-up periods, and statistical power parameters must be verified by the researcher after confirming their availability within the official functional scope.