2025 World Transplant Congress Abstract
AI-Driven Comparative Analysis of Transplant Education Materials
Overview
A published research abstract based on a Carnegie Mellon University capstone project analyzing transplant center patient education materials using natural language processing and generative AI.
Problem
Transplant education handbooks vary significantly across institutions in both clarity and coverage of essential topics. This inconsistency may affect patient understanding, informed consent, and post-transplant outcomes.
Approach
Extracted and analyzed handbook content from major transplant centers using NLP techniques and generative AI. Structured the text for comparative analysis to assess topic inclusion, clarity, and variability across sites.
Results & Impact
The analysis revealed substantial discrepancies in essential topic coverage and clarity across transplant centers, supporting the need for standardized, evidence-based educational materials to improve patient comprehension and long-term transplant outcomes.