Researchers at the California Institute of Technology (Caltech) have developed an innovative artificial intelligence algorithm called CellSAM (Cell Segment Anything Model), designed to identify cells in biological images efficiently. This tool addresses the traditionally labor-intensive process of manually labeling cells, which previously consumed countless hours of researchers' time. The interdisciplinary team, led by David Van Valen (assistant professor of biology) and Yisong Yue (professor of computing), published their findings in the journal Nature Methods. Van Valen emphasized the potential of CellSAM to push the boundaries of biological discovery by allowing researchers to collect and analyze complex data more effectively. The algorithm is versatile, capable of being applied across various biological contexts, from identifying tumor cells to understanding immune cell interactions. Currently, CellSAM is freely accessible to researchers, facilitating broader exploration of biological questions at scales that were once impractical. The team plans to enhance the algorithm further by training it on more diverse biological datasets. Funding for the project came from multiple organizations, including the National Institutes of Health and the Gordon and Betty Moore Foundation.