AI-based methods for the assessment of DNA damage and repair mechanisms

In recent years, a growing number of artificial intelligence (AI)-driven approaches have been developed to elucidate chemico-biological interactions associated with DNA damage and oxidative stress. Deep learning-based techniques, in particular, have demonstrated substantial potential within molecular biology and toxicology. As a result, researchers and clinicians alike hold high expectations that AI-enabled tools will soon make meaningful contributions to our understanding of the molecular and cellular mechanisms governing DNA damage and repair. In this article, we present a concise yet comprehensive overview of the computational methodologies underpinning contemporary deep learning approaches. We examine their capacity to support DNA damage assessment by revealing mechanistic insights into damage induction and response pathways. Particular emphasis is placed on deep learning techniques designed to enhance the analysis of complex biological data, including the automated detection and quantification of DNA damage from comet assay images and microscopy-based platforms. Furthermore, we critically assess the extent to which a gap exists between the expectations of researchers, biologists, and clinicians and the current practical capabilities of AI technologies in this domain. Finally, we offer a forward-looking perspective on how this gap might be narrowed, outlining key methodological, data-driven, and translational challenges that must be addressed to fully realize the potential of AI in DNA damage and repair research.