NIH awards one of the year’s seven Pioneer grants to an AI system that generates cancer hypotheses and tests them robotically in patient tissue
NIH awards one of the year’s seven Pioneer grants to an AI system that generates cancer hypotheses and tests them robotically in patient tissue
On October 6, 2026, the US National Institutes of Health (NIH) named Olivier Elemento, director of the Englander Institute for Precision Medicine at Weill Cornell Medicine, one of just seven recipients of its most selective grant, the NIH Director’s Pioneer Award. The five-year grant of nearly $6 million will fund an AI system that independently generates hypotheses about cancer mechanisms from human cell data. A robotic platform will then test those hypotheses in parallel in tumor organoids, miniature versions of tumors grown from patients’ own cells. The system will first be applied to lung cancer.
Since 2004, the NIH Director’s Pioneer Award has funded some of the country’s highest-risk biomedical research, supporting ambitious ideas before they produce results. In 2026, seven people across all fields of science received the award; the National Cancer Institute administers Elemento’s grant.
For decades, we have updated our tools while keeping the same scientific method. This award allows us to change the method itself.
Elemento explains this in the press release about the award. AI agents analyze data from human cells and tissues and independently propose a hypothesis about a disease mechanism, such as which mutation triggers tumor growth. The system is trained on data from direct interventions in living cells, such as switching off a gene and observing what happens. These data allow it to look for causal mechanisms of cancer by linking an intervention to its effects, rather than relying on correlations alone. A virtual model trained on these responses first tests the hypothesis computationally at low cost. A robot then runs hundreds of experiments in parallel on organoids, three-dimensional tumors measuring fractions of a millimeter, instead of mice. Running experiments in parallel addresses the slow pace of sequential animal tests. The results of each round feed back into the system to guide its next step. Different agents have different roles: one proposes hypotheses, another critiques them, a third plans experiments, and a fourth analyzes the data. Scientists retain final authority and reject unsafe or pointless steps.
A discovery requires more than a prediction. We want the system to propose, test, and revise biological mechanisms, just as scientists do.
This is how Elemento responds to earlier attempts to build an “AI scientist”: in May, FutureHouse’s Robin searched the literature for hypotheses, while humans performed the experiment on retinal cells; the Medra robot executes a protocol, while a scientist supplies the hypothesis. In Elemento’s approach, the system itself generates the hypothesis, and a robot tests it in organoids. This replaces testing in mice, whose results are not guaranteed to translate to humans. The institute has already assembled more than 300 models of tumor organoids covering a dozen cancer types, along with an extensive collection of data on lung cancer, the project’s first target. Its five-year survival rate is below 3 in 10, and computed tomography scans often reveal hazy patches: early lesions that develop into invasive cancer in some patients but remain harmless in others.
Elemento has authored more than 500 papers, appeared on Clarivate’s list of the world’s most highly cited researchers from 2019 to 2024, and has led the institute since 2017. In August 2025, he wrote in STAT News that medical AI must undergo the same randomized trials as drugs, and he has applied the same rigor to his own system.
In five years, we will see whether this closed-loop approach can identify causal mechanisms of cancer faster and more reliably than existing approaches, and produce an open protocol that other laboratories can reproduce.