Closing the Loop in AI-Driven Biomedical Discovery

AI scientists generate hypotheses, propose experiments, and analyze datasets, and several have produced findings that were confirmed in the laboratory. Although they are opening a path to autonomous discovery, the loop from hypothesis to experiment to revised hypothesis is still often anecdotal. Here, we consider how that loop could be closed to enable AI-driven biomedical discovery. Closing the loop requires AI scientists that reason over biomedical knowledge and data across horizons of days to months. It also requires verifying hypotheses with computational tools and experiments in biological laboratories and clinical environments. Agentic and reasoning methods need to learn over time from sparse, delayed, and noisy feedback that verifiers return. In mathematics and program search, where candidate solutions can be verified at negligible cost and often as soon as they are generated, methods that generate and score large numbers of candidates have improved bounds on problems that had been open for decades. In biomedical sciences, comparable progress requires closing the loop where each test is slow, costly, and partially informative. Closed-loop AI-driven biomedical discovery can enable a mode of research in which AI continuously reviews hypotheses against accumulating experimental results. Scientists choose which experiments to pursue, and AI maintains a record that links each result to the hypothesis that produced it.