Abstract A018: Identifying and integrating global ovarian cancer patient priorities to guide artificial intelligence (AI) research in ovarian cancer

Abstract Background: Ovarian Cancer Research Alliance (OCRA; USA), Ovarian Cancer Action (OCA; UK), Ovarian Cancer Canada (OCC; Canada) and the Ovarian Cancer Research Foundation (OCRF; Australia) have launched the Global Ovarian Cancer Research Consortium. The Consortium unites four leading ovarian cancer research funding organizations to combine resources, expertise and determination to accelerate progress where it’s desperately needed. The Consortium’s inaugural joint initiative is a $1M (USD) international research grant program to harness the power of Artificial Intelligence (AI) to improve ovarian cancer outcomes. To ensure this program aligns with patient needs, the Consortium sought to understand research priorities from individuals with lived experience of ovarian cancer, the results of which will help inform this funding call. Objectives: To understand priorities from people with lived experience of ovarian cancer, in the context of applying AI to improve outcomes, and to support direction of the AI Accelerator Grant funding round. Methods: Each organization conducted a survey to collect qualitative data from their respective patient and public involvement (PPI) networks. In total, there were 657 respondents: 62% diagnosed with ovarian cancer, 19% caregivers, and 19% others affected. Responses were pooled and thematically analyzed to identify shared priorities. Results: Consistent research priorities emerged across all geographies: Early detection: reliable early detection tools or screening tests, biomarker research, symptom awareness to support earlier clinical intervention. Better treatments: more effective and less toxic options, targeted therapies, immunotherapy, alternatives to chemotherapy, personalized medicine. Disease recurrence: recurrence drivers, methods to detect, delay or prevent recurrence, supportive care for those living with recurrent or incurable disease. Risk & prevention: improved risk prediction for those with a family history or known genetic mutation, expanding preventative strategies. Respondents in the UK were additionally asked about the use of AI in ovarian cancer research. Between 75-80% expressed positive or broadly supportive views. Perceived benefits included the ability to analyze large, complex patient datasets, identify recurrence risk or treatment responses faster, and pattern recognition that may not be identifiable to the human eye. The most common concern expressed was that AI should not replace compassionate care or clinical judgment. Conclusions: People affected by ovarian cancer are broadly supportive of the use of AI in ovarian cancer research to accelerate progress in a cancer type with limited treatment options and poor survival rates. While broadly optimistic, patients emphasized that AI must complement, not replace, human oversight in clinical settings. Embedding lived experience into research funding strategies, including emerging fields like AI, ensures that innovation is patient-informed, ethically grounded, and directed toward the areas of greatest need. Citation Format: Sarah DeFeo, Faye Hobbs, David Hunt, Jessica Lawson, Kristin McGowan, Marie-Claire Platt, Alicia Tone, Amy Wilson. Identifying and integrating global ovarian cancer patient priorities to guide artificial intelligence (AI) research in ovarian cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Ovarian Cancer Research; 2025 Sep 19-21; Denver, CO. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl):Abstract nr A018.