AI Collaborative

Community Form

Mapping AI innovation across the organ donation and transplantation community to drive visibility, knowledge-sharing, and collaboration nationwide.

The Scientific Registry of Transplant Recipients (SRTR) has developed a comprehensive Transplant System Map that outlines key milestones across the transplant journey—from donor identification (both deceased and living donors) through post-transplant outcomes. This framework provides a shared view of the complex processes and decision points that define the field.

As organ procurement organizations (OPOs), hospitals, and industry partners increasingly explore the use of artificial intelligence—through research, pilot programs, and full-scale implementation—the Organ Donation and Transplantation Alliance AI Transplant and Donation Resource Collaborative is working to map these efforts across the SRTR Transplant System. The goal is to create visibility into ongoing innovation, support knowledge-sharing, and accelerate collaboration across the national community.

If your organization is currently utilizing AI in organ donation or transplantation, please complete the form below to share your work. Submissions will be featured in the table below to help inform and connect the broader community.

Patient Journey Ai Resource Hub
AI Innovation Form

Select the one journey stage that best correlates with your submission. If your submission is related to multiple journey stages or falls between multiple journey states, select the stage that best correlates or the stage at the beginning of the process.

Include a specific website that highlights your project or publication.
Before a full description of your project, please include a one-sentence summary. This will be displayed publicly on the submission listing page. Submissions must be educational and evidence-based, emphasizing outcomes, methodology, and key learnings rather than the promotion of a product, service, or organization. Content that is primarily promotional in nature will not be accepted. Descriptions should remain objective and free of promotional language or superlative claims (for example, “the best,” “the first,” “the only,” “the fastest,” or “the most accurate”).

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