I-Corps: Translation Potential of a Personalized Interactive Artificial Intelligence (AI) System for Living Kidney Donor Engagement

The broader impact of this I-Corps project is based on the development of a Generative Pre-trained Transformer (GPT) system specifically designed to help potential kidney donors access personalized answers during their online information-seeking journey. This technology can help transplant centers attract more live kidney donors, which is essential for achieving superior transplant outcomes for patients. This technology could prove to be a significant cost-effective strategy for Medicare as the main payer of kidney care in the country, while enabling doctors and hospitals to achieve better outcomes for patients. The lack of comprehensive and personalized online material about kidney transplant and kidney donation has been a major barrier for potential kidney donors to complete the donation process. This project aims to lower this barrier by introducing accurate and traceable generative artificial intelligence (AI) patient support, coupled with live-donor mentors offered by transplant centers, to increase the live kidney donation rate and save more patients. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. The solution is based on the development of a well-trained Generative Pre-trained Transformer (GPT) AI system with a comprehensive dataset encompassing all kidney transplant and donation related topics. Existing GPT models are not specialized in the field of kidney donation and this technology would change that. Online information from kidney transplant centers and other professional transplant-related organizations will create a well-trained GPT model with a comprehensive dataset. A context-aware prompts-generation (CAPG) model enables personalization of the GPT responses. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.