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Organ-Chips are micro-engineered systems that aim to recapitulate the organ microenvironment. Implementation of Organ-Chips within the pharmaceutical industry aims to improve the probability of success of drugs reaching late stage clinical trial by generating models for drug discovery that are of human origin and have disease relevance. We are adopting the use of Organ-Chips for enhancing pre-clinical efficacy and toxicity evaluation and prediction. Whilst capturing cellular phenotype via imaging in response to drug exposure is a useful readout in these models, application has been limited due to difficulties in imaging the chips at scale. Here we created an end-to-end, automated workflow to capture and analyse confocal images of multicellular Organ-Chips to assess detailed cellular phenotype across large batches of chips. By automating this process, we not only reduced acquisition time, but we also minimised process variability and user bias. This enabled us to establish, for the first time, a framework of statistical best practice for Organ-Chip imaging, creating the capability of using Organ-Chips and imaging for routine testing in drug discovery applications that rely on quantitative image data for decision making. We tested our approach using benzbromarone, whose mechanism of toxicity has been linked to mitochondrial damage with subsequent induction of apoptosis and necrosis, and staurosporine, a tool inducer of apoptosis. We also applied this workflow to assess the hepatotoxic effect of an active AstraZeneca drug candidate illustrating its applicability in drug safety assessment beyond testing tool compounds. Finally, we have demonstrated that this approach could be adapted to Organ-Chips of different shapes and sizes through application to a Kidney-Chip. [ABSTRACT FROM AUTHOR]