<p>Precise assessment of cell growth, count, and viability is a prerequisite for biological and medical research. Traditional cell analytics involve manual processes, such as cell counting or reagent-based approaches that are user-dependent and prone to bias. Semi-automated systems for counting cells, tracking cell growth, and determining viability have been introduced over the past decades. However, these methods are often time-consuming, require labeling steps, and involve costly instrumentation and consumables. Changes in cell growth and/or viability create biological patterns that can be interpreted by artificial intelligence (AI). Here, we report the development and validation of SnapCyte™, an AI application that performs accurate, unbiased, label- and reagent-free cell analyses from basic cell culture images. Using cell lines with diverse morphologies in various culture conditions, we generated a comprehensive and fully annotated image database that was used for AI education. Convolutional neural networks were employed for cell localization and iterative training loops until a stable performance of &gt;95% accuracy was obtained for all readouts. The fully trained AI demonstrated high Precision and Recall and performed with greater accuracy and less variation as compared to standard methods. As the SnapCyte analyses are performed on cell images only, data acquisition is non-invasive to the experimental setup, enabling real-time use of cells in downstream assays. In summary, SnapCyte is a fast and accurate cell analytics platform, resistant to user variations and independent of reagents or specific equipment, with improved performance over current cell analytics methodologies.</p>