Artificial intelligence is increasingly being explored for gynaecological imaging. In June 2026, researchers presented Female-RHINO, a research framework designed to perform quantitative pelvic MRI analysis and generate structured reports.

The system combines communication with the MRI scanner and deep-learning models that can segment structures and identify anatomical landmarks to derive quantitative biomarkers. The work was evaluated using more than 500 multicentre datasets covering different scanners, vendors and imaging protocols.

What could automation add?

More standardised measurements could help reduce observer variability and make studies easier to compare. It may also make selected anatomical parameters more reproducible and easier to monitor over time.

A technology still under research

The results are promising, but Female-RHINO remains a research tool and should not be considered a replacement for specialist assessment. Further validation and rigorous evaluation of safety and clinical usefulness are needed before systems of this type can enter routine care.

Source: Bhatia et al., Female-RHINO, 23 June 2026.