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Articles linked to this work by shared authors, journal, and citation graph.

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Related Experiment Video

Updated: May 31, 2025

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Gender-Equity Model for Liver Allocation Using Artificial Intelligence (GEMA-AI) for Waiting List Liver Transplant

Antonio Manuel Gómez-Orellana1, Manuel Luis Rodríguez-Perálvarez2, David Guijo-Rubio3

  • 1Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Spain; Instituto Maimónides de Investigación Biomédica de Córdoba, Córdoba, Spain.

Clinical Gastroenterology and Hepatology : the Official Clinical Practice Journal of the American Gastroenterological Association
|January 23, 2025
PubMed
Summary
This summary is machine-generated.

A new artificial intelligence score, GEMA-AI, accurately predicts liver transplant waiting list outcomes, especially for sicker patients and women. This advanced model offers improved prioritization over existing methods, potentially saving lives.

Keywords:
Artificial Neural NetworksDisparitiesExplainable Artificial IntelligenceGenderLiver AllocationMachine Learning

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Area of Science:

  • Hepatology
  • Artificial Intelligence
  • Medical Informatics

Background:

  • Liver transplantation (LT) waiting list prioritization models are crucial for equitable organ allocation.
  • Existing models may not fully capture the complexity of patient outcomes.
  • There is a need for improved predictive accuracy in identifying patients at highest risk.

Purpose of the Study:

  • To develop and validate an artificial intelligence (AI) score, GEMA-AI, for predicting LT waiting list outcomes.
  • To compare GEMA-AI's performance against established models using identical input variables.
  • To assess the impact of a nonlinear AI approach on patient prioritization.

Main Methods:

  • A cohort study of adult LT candidates in the UK (2010-2020) for training/internal validation and Australia (1998-2020) for external validation.
  • GEMA-AI utilizes an explainable artificial neural network incorporating international normalized ratio, bilirubin, sodium, and glomerular filtration rate.
  • Comparison with GEMA-Na, MELD 3.0, and MELD-Na for waiting list prioritization.

Main Results:

  • GEMA-AI demonstrated superior discrimination compared to GEMA-Na, MELD-Na, and MELD 3.0 in both internal and external validation cohorts.
  • The AI model showed a more pronounced benefit in women and patients with extreme analytical values.
  • Transitioning to GEMA-AI could re-prioritize 6.4% of patients and potentially save 1 in 59 deaths overall, including 1 in 13 deaths among women.

Conclusions:

  • Explainable machine learning models like GEMA-AI may outperform conventional regression-based models for LT waiting list prioritization.
  • GEMA-AI provides more accurate predictions of waiting list outcomes, particularly for critically ill patients.
  • The study highlights the potential of AI to enhance the fairness and effectiveness of organ allocation systems.