The scope of this research will encompass the application of AI in interpreting complex radiological images and clinical data to diagnose gynecological cancers more effectively and efficiently. This research aims to fill the gap by developing a specialized VQA model for gynecological cancers such as ovarian and cervical. The successful implementation of this project could revolutionize how cancers such as ovarian cancer are diagnosed, reducing the rate of late-stage discovery and improving survival rates. It also promises to reduce diagnostic wait times and enhance the overall efficiency of radiological assessments, thereby supporting more equitable health outcomes. This research is expected to significantly improve diagnostic processes for gynecological cancers, such as reducing late-stage diagnosis rates and early menopause and support more effective treatment approaches. It also aims to establish a new standard for integrating AI into clinical practice, providing a replicable model for other cancers​.

Sarah Taleghani, Health System Management and Health Data Analytics MA student