AI could transform ovarian care through personalisation, study finds

AI could transform ovarian care by personalising cancer and fertility treatment, but more clinical validation is needed before routine use.

A systematic review and meta-analysis found AI models showed high diagnostic accuracy for ovarian cancer when combining data such as ultrasound scans and blood test results.

Across 81 studies, AI models correctly identified ovarian cancer in around nine out of 10 cases, with pooled rates of 89 to 94 per cent.

They were also highly accurate at ruling out ovarian cancer when it was not present, with specificity of 85 to 91 per cent.

The analysis also found that explainable AI tools could predict complete surgical cytoreduction in advanced ovarian cancer.

Complete surgical cytoreduction means removing all visible cancer during surgery, which can be an important goal in treatment planning.

The tools achieved a pooled AUC of 0.87. AUC is a measure of how well a model distinguishes between different outcomes, with higher scores showing stronger performance.

In reproductive medicine, AI algorithms helped physicians optimise ovarian stimulation protocols and predict follicular growth during IVF.

Ovarian stimulation is the use of hormones to encourage the ovaries to produce eggs, while follicles are the small sacs in the ovaries where eggs develop.

The review found AI could reliably model ovarian response in IVF with a pooled AUC of 0.81.

However, researchers said challenges remain in translating promising research findings into routine clinical practice.

They identified substantial variation across studies, driven by retrospective study designs, variable AI systems and a lack of standardised validation.

Only 22 per cent of analysed studies reported prospective, multicentre external validation, where models are tested forward in time across multiple healthcare settings.

The authors called for rigorous validation to help close the gap between research and routine clinical practice, alongside standardised methodological and reporting frameworks, smooth integration with clinical workflow and robust governance to support responsible and ethical AI use.

They concluded: “Artificial intelligence is a transformative force in the management of ovarian conditions.

“In gynaecologic oncology, AI enhances every phase of care, from early detection and accurate diagnosis to prognostic stratification and surgical planning.”

In reproductive medicine, AI personalises ovarian stimulation and refines the diagnosis of heterogenous endocrine disorders such as PCOS.

PCOS, or polycystic ovary syndrome, is a hormonal condition that can affect periods, skin, weight and fertility.