Artificial intelligence in bioanalytics: implications for research and education

Artificial intelligence (AI) is increasingly shaping bioanalytical research by supporting the analysis of complex, high-dimensional and multimodal datasets. Although AI-based methods often achieve remarkable predictive performance, their scientific relevance ultimately depends on the extent to which model outputs can be linked to biologically, chemically, and physically meaningful processes. This article discusses the implications of AI-supported bioanalytics for both research and higher education, with particular emphasis on model transparency, interpretability, and analytical validity. Black-box, grey-box, and white-box models are considered as distinct yet complementary approaches to knowledge generation. In this context, grey-box and hybrid modelling strategies are identified as especially suitable for bioanalytical applications because they integrate data-driven learning with established domain knowledge. The discussion is illustrated by a case study on UV/Vis spectroscopic monitoring of mammalian cell viability, demonstrating how physics-informed AI, experimental design, spectral analysis, and rigorous model validation can contribute not only to reliable predictions but also to a more mechanistic interpretation of bioanalytical data. From an educational perspective, the integration of AI extends beyond the acquisition of technical skills and requires curricula that foster critical reasoning, uncertainty assessment, and the reflective evaluation of model results. Responsible use of AI in bioanalytics therefore requires its treatment as a tool that complements scientific judgement rather than replacing analytical expertise.