Artificial intelligence in experimental and clinical in vitro analysis: applications, limitations, and future directions.

The integration of artificial intelligence into in vitro analyses represents a transformative shift in laboratory medicine, diagnostics, and pharmaceutical research. Traditional in vitro methods, from cell culture assays to high-throughput screening, rely on manual interpretation and conventional statistics. Machine and deep learning now automate image analysis, improve diagnostic accuracy, accelerate drug discovery, and enhance quality control. Convolutional neural networks and predictive models have reported specialist-level performance in cell segmentation, biomarker detection, and pathological classification, although mainly in internal benchmark studies rather than in prospective clinical validations. However, important limitations persist, including dependence on data quality and standardization, limited external and prospective validation, algorithmic transparency, ethics and data governance, regulation, and unequal access to computing infrastructure. We examine the applications, limitations, and future directions of this technology. In this review, in vitro analysis refers to the laboratory examination of cells, tissues, and biological samples outside the living organism, and it spans both experimental discovery research and clinical in vitro diagnostics, which share analytical challenges but differ in their validation and regulatory requirements.