Artificial Intelligence in Gynecologic Oncology: Current Applications, Clinical Challenges, and Future Perspectives

Cervical, endometrial, ovarian, vulvar, vaginal, fallopian tube, and gestational trophoblastic neoplasia (GTN) are major gynecologic cancers that significantly impact women's health globally. In spite of progress in surgery, chemotherapy, radiotherapy, and targeted therapies, results are still variable, and timely diagnosis frequently proves challenging. Artificial intelligence (AI) has progressively taken advantage of, in digital pathological conditions risk prognostication for gynecological pathologies like endometrial and ovarian cancers, and automated Pap smear clarification for cervical cancer. Multi-platform methods combining clinical approaches and imaging data may help with prognostic assessments and personalized therapies. Nevertheless, major clinical information arises from single-center retrospective studies with minimal external authentication, and challenges like data heterogeneity, the lack of systematized protocols, ethical concerns, algorithmic bias, transparency, and workflow integration must be addressed in light of wide-ranging clinical and scientific approval.