Featured Application: The OC_Identifier is presented and evaluated with regard to its potential use to extend or even replace the biochemical analysis of osteoclast maturation in cell cultures. A form of AI was developed and trained to classify four different cell types, with a particular focus on identifying, counting, and determining the maturity of osteoclasts. Osteoclasts, formed by the fusion of monocytes, show clear morphological differences in their maturation, from small mononuclear cells to large multinuclear cells. The developed AI used YOLOv5m models to analyze these cell types based on microscopic images. The AI showed a certain degree of correlation with biochemical analyses (TRAP 5b, CAII). Despite this success, several challenges were identified. The homogeneity of the training data, limited by standardized cell culture conditions, limited the coverage of all osteoclast properties. Furthermore, the AI did not take into account the number of cell nuclei or the specific amount of DNA in the cells, which impaired the precision of the analysis of multinucleated osteoclasts. In the future, the introduction of weighting factors for cell nuclei could optimize the agreement of AI results with biochemical analyses. In summary, the developed AI technology offers a promising tool for cell identification and analysis, especially in osteoclast research. With further developments, this technology could significantly increase the efficiency and accuracy of cell analysis and promote practical applications in research and diagnostics.