This white paper explores how artificial intelligence, machine learning, and deep learning are applied to cellular imaging to improve drug discovery productivity. It explains key concepts and traces the evolution of AI-driven image analysis in biomedical research. Applications include high-content screening, phenotypic profiling, 3D segmentation, and bias reduction in image interpretation. The paper highlights how ML/DL dramatically reduce analysis time, improve accuracy, and uncover insights not accessible through traditional methods. Integrated platforms such as Signals Image Artist and Signals Research Suite enable scalable analysis of large imaging datasets, accelerating discovery and improving translational outcomes.

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