AI innovations for ovarian and endometrial cancer diagnosis: Methodological challenges and engineering roadmap

The late-stage presentation of ovarian and endometrial cancers, along with varied imaging characteristics and screening constraints, hinders early diagnosis. Representative studies indicate accuracies ranging from 85% to 96% and AUCs reaching 0.99; however, the majority depend on single-center, retrospective datasets that lack external validation. Class imbalance, lack of explainability, high computational needs, and problems with clinical integration are some of the most important engineering problems. We emphasize solutions such as focal loss augmentation, saliency map stabilization, and model compression (quantization/pruning resulting in a 4-10 × reduction in size with less than 3% accuracy loss). Federated learning for multi-site data, edge-AI deployment (sub-second ultrasound triage), and hardware optimization to make real-world translation possible are all important areas for future research. For AI to change how gynecologic oncology is diagnosed, it needs to go through strict prospective validation and interdisciplinary engineering.