Homologous recombination deficiency (HRD) is a crucial biomarker for guiding treatment in breast and ovarian cancers, but current molecular tests are often costly and time-consuming. This review examines three key studies that explore innovative approaches to HRD diagnosis. The potential of artificial intelligence (AI)-based deep learning shows that a model trained on standard histopathological images can predict HRD and correlate with better clinical outcomes in patients treated with platinum-based therapies. And this model identified a broader, functionally relevant HRD phenotype than traditional methods, potentially expanding the patient population eligible for treatment. The other study highlights a major challenge: model generalizability. Their model, while effective on an internal dataset, showed a significant drop in performance on an external cohort due to technical and biological variability. This underscores the need for larger, multi-institutional datasets for robust clinical translation. Complementing these AI-based approaches, a third study presents a novel functional assay that directly measures a tumor’s current HR capacity. This approach offers a valuable alternative by providing a real-time snapshot of tumor biology, which may be more reflective of chemosensitivity than historical genomic alterations. Together, these studies suggest a synergistic future for HRD diagnosis, where AI tools serve as a rapid, accessible screening method, and functional or traditional molecular tests are used for confirmation. The goal is to develop robust, generalizable AI models that can fundamentally change precision oncology and democratize access to life-saving treatments.