Unlike many other malignancies, overall survival for women with epithelial ovarian cancer has improved only modestly over the last half-century. The perspectives presented here detail the views of a gynecologic oncologist looking back and the view of the academic editor looking forward. Surgical beginnings in 1809 are merged with genomics, surgical advances, and precision therapy at present and for the future. Presentations in this special issue focus on factors related to the diagnosis of ovarian cancer: (1) markers for the preoperative assessment of primary and metastatic ovarian tumors, (2) demonstrations of the presence of pelvic fluid in ultrasound studies of ovarian malignancies, (3) the effects of age, menopausal status, and body habitus on ovarian visualization, (4) the ability of OVA1 to detect ovarian cancers when Ca125 was not informative, (5) the detection of tumor-specific changes in cell adhesion molecules by tissue-based staining, (6) presentation of a high discrimination model for ovarian cancer using IOTA Simple Rules and CA125, (7) review of low-grade serous carcinoma of the ovary, and (8) a comprehensive case report on ovarian carcinosarcoma.

Publications on the application of artificial intelligence (AI) to many situations, including those in clinical medicine, created in 2023–2024 are reviewed here. Because of the short time frame covered, here, it is not possible to conduct exhaustive analysis as would be the case in meta-analyses or systematic reviews. Consequently, this literature review presents an examination of narrative AI’s application in relation to contemporary topics related to clinical medicine. The landscape of the findings reviewed here span 254 papers published in 2024 topically reporting on AI in medicine, of which 83 articles are considered in the present review because they contain evidence-based findings. In particular, the types of cases considered deal with AI accuracy in initial differential diagnoses, cancer treatment recommendations, board-style exams, and performance in various clinical tasks, including clinical imaging. Importantly, summaries of the validation techniques used to evaluate AI findings are presented. This review focuses on AIs that have a clinical relevancy evidenced by application and evaluation in clinical publications. This relevancy speaks to both what has been promised and what has been delivered by various AI systems. Readers will be able to understand when generative AI may be expressing views without having the necessary information (ultracrepidarianism) or is responding as if the generative AI had expert knowledge when it does not. A lack of awareness that AIs may deliver inadequate or confabulated information can result in incorrect medical decisions and inappropriate clinical applications (Dunning–Kruger effect). As a result, in certain cases, a generative AI system might underperform and provide results which greatly overestimate any medical or clinical validity.