Label-free imaging offers considerable benefits compared to traditional fluorescence-based methods, including faster processing, reduced reagent costs, and the ability to monitor live cells. Label-free detection also offers key advantages by allowing observation of biomolecular interactions without sample modification, resulting in more accurate data, deeper characterisation, and real-time monitoring of cellular dynamics. This article outlines the use of customisable artificial intelligence (AI) models within IN Carta® image analysis software to develop deep-learning-based segmentation models suitable for accurate cell and nuclear detection that only use transmitted-light (TL) images. Label-free analysis was initially established using the U2OS cell line. A pre-trained AI model was first applied to segment nuclei in Hoechst-stained images, enabling the development of a TL-based nuclear segmentation model. The resulting segmentation masks were paired with corresponding TL images and used to train a new model able to segment nuclei using only TL images. This approach enabled label-free nuclear detection with over 97% accuracy, as validated against Hoechst-based nuclear counts. The model was also able to reliably quantify both damaged and live cell phenotypes. The model was further trained using images from additional cell lines, including HCT116, U2OS, HeLa, MCF7, and HEK. The model achieved more than 90% detection accuracy across the majority of these cell types. The five cell lines were treated with 10 anti-cancer compounds using a seven-point dilution series to evaluate the model’s utility in compound screening. Cell counts acquired via TL-based nuclear segmentation were compared against conventional nuclear staining methods across treatments. The results showed strong correlation and comparable IC50 values, verifying the label-free approach’s accuracy and reliability. This label-free approach offers important advantages in streamlined processing speed and workflow simplicity, allowing real-time analysis of compound effects. This makes the approach a powerful tool in high-throughput and phenotypic screening applications. Label-free detection and analysis offer a range