Evaluating Drug Eruptions Using AI: Tips From Alina G. Bridges, DO Artificial intelligence (AI) is a promising adjunct in dermatopathology, enhancing detection of subtle histologic features in drug eruptions and enabling standardized quantification through whole-slide image analysis. Techniques such as attention-based models and heatmap generation may highlight focal or overlooked findings, but drug eruption–specific validation remains limited. Cutis board member Alina G. Bridges, DO, discusses AI’s role as an assistive tool, emphasizing explainability, human oversight, documentation, and robust data governance to ensure safe, effective implementation. How might AI enhance the detection of key histologic features in drug eruptions compared to traditional microscopy? DR. BRIDGES: AI offers the potential to enhance detection of histologic features in drug eruptions by systematically analyzing entire whole-slide images. Convolutional neural networks and attention-based models can identify subtle or focal findings such as scattered dyskeratotic keratinocytes, focal spongiosis, early interface change, rare eosinophils, or microvascular injury, which may be overlooked during routine microscopy due to sampling limitations. This capability is particularly relevant in drug eruptions, where histologic changes often are heterogeneous and patchy. AI-generated attention heatmaps can highlight diagnostically relevant regions across the slide, improving consistency and completeness of slide reviews. While AI has demonstrated high sensitivity and specificity in broader dermatopathology tasks, particularly neoplastic conditions, drug eruption–specific validation data are currently lacking. As such, the most realistic application at present is AI functioning as a sensitivity-enhancing adjunct or “second reader,” improving consistency and completeness of slide review while preserving expert human interpretation. Which histologic patterns in drug eruptions are hardest to quantify, and how could AI help standardize their assessment? DR. BRIDGES: AI-based image analysis can standardize the assessment of histologic patterns through objective reproducible quantification. Deep learning algorithms can segment epidermal and dermal compartments, identify inflammatory cell types, and calculate metrics such as eosinophil