Standardized and AI-Assisted Ultrasound Triage for Ovarian Cancer Diagnosis: IOTA, O-RADS and Clinical Governance

Purpose: To review how standardized ultrasound-based risk stratification and artificial-intelligence (AI) governance have reshaped early diagnostic pathways for ovarian cancer. Materials and Methods: We performed a critical narrative review of milestones in transvaginal ultrasound, IOTA descriptors, ADNEX, O-RADS US/MRI, population screening trials and AI reporting/governance standards. Priority was given to evidence that used external validation, calibration or explicit links between risk categories and clinical management. Results: Population screening with CA-125 and ultrasound has not reduced mortality in average-risk women; therefore, the highest near-term value lies in triage once an adnexal lesion is detected. IOTA/ADNEX and O-RADS translate sonographic descriptors into reproducible probabilities and management categories; O-RADS MRI is useful for sonographically indeterminate lesions. AI may support segmentation, structured reporting and triage, but high discrimination is insufficient without external validation, calibration, transportability testing, decision-curve analysis and subgroup monitoring. Conclusion: Earlier and safer decisions are more likely to come from living standards - structured ultrasound reporting, calibrated risk models, MRI arbitration, auditable AI updates and equity-aware monitoring - than from population screening alone.