DEVELOPING PARTNERSHIPS AND COLLABORATIONS TO ADVANCE AI
Discussion Points ▪ Barriers to partnerships and collaborations in AI health research, including data sovereignty, governance, and access issues ▪ Challenges in transitioning from research to implementation, with many projects stalling at prototype stage ▪ Need for synthetic data to enable innovation while preserving privacy ▪ Importance of building trust with patients and communities regarding data use ▪ Funding gaps between research and commercialisation phases ▪ Challenges of siloed systems and lack of interoperability across health sector ▪ Value of person-centred approaches when developing AI solutions ▪ Potential for AI to reduce administrative burden and improve efficiency Key Actions ▪ Explore creation of a project registry/library to track AI initiatives and prevent duplication ▪ Consider developing a framework for evaluating AI tools and solutions ▪ Investigate synthetic data approaches to enable innovation while protecting privacy ▪ Establish clearer pathways from research to implementation with defined stage gates ▪ Develop better methods for quantifying the value of AI implementations to justify investment ▪ Engage clinicians and end-users earlier in the development process Additional Notes ▪ Success stories shared included Microsoft Copilot implementation at Ministry of Health ▪ Breast Cancer Foundation's approach of bringing researchers in-house rather than sharing data externally ▪ Discussion of international examples and opportunities for cross-border collaboration ▪ Recognition that AI tools should augment rather than replace clinical decision-making ▪ Acknowledgement that cultural considerations and Te Ao Māori perspectives must be integrated into AI development
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