Artificial Intelligence for Ovarian Health Assessment: A Survey of Ultrasound, Multimodal Learning, Explainable AI and Longitudinal Approaches

Evaluating the health of ovaries demands corroboration across symptoms, menstrual and reproductive history, lab values, ultrasound imagery, and prior clinical encounters. We review recent artificial-intelligence work surrounding ovarian health across ultrasound analysis, clinical natural- language processing, multimodal learning, explainable AI, uncertainty estimation, and longitudinal modelling. Our work included researching recent indexed, peerreviewed literature with targeted searches, screening the results, and analyzing two datasets based on modality, task, algorithm, dataset attributes, validation strategies, explainability, and temporal modelling. Current literature is advancing rapidly in image-based and multimodal ovarian diagnosis but also exhibits several key consistent limitations including retrospective datasets with little to no external validation, heterogeneous labels, incomplete patient-level multimodal pairings, and reliance on cross-sectional prediction. Although the volume of longitudinal-specific ovarian AI is sparse in comparison, recent research illustrates that longitudinal modelling can be done explicitly. Through our analysis we identify an opportunity for a connected, doctor-facing system that leverages clinical text, sonography reports and ultrasound images, other medical or lab reports, visit-level structured evidence, and patient-level longitudinal reasoning. From here we motivate OvaCare, whose output for each encounter can be broken down into two parts: OUTPUT 1, a structured clinical insight contributed to a patient timeline, and OUTPUT 2, a longitudinal clinical insight generated from analyzing subsequent visits. Ultimately, the end result of this review is not only a current summary of work in ovarian-focused AI, but also provides research-gapdriven justification for OvaCare 's multimodal and temporal methodologies.