Artificial intelligence in liver transplantation: Opportunities and challenges

Area of Science:

• Hepatology
• Transplant Surgery
• Medical Informatics

Background:

• Liver transplant management involves complex interactions of pre, peri, and postoperative factors, making outcome prediction challenging.
• Artificial intelligence (AI) offers potential solutions for decision-making throughout the liver transplantation process.

Purpose of the Study:

• To explore the multifaceted role of AI in liver transplantation, from diagnosis and candidacy assessment to post-operative care and outcome prediction.
• To highlight AI's potential to improve organ matching, personalize treatment, and enhance surgical precision.

Main Methods:

• AI algorithms analyze large datasets of donor and recipient characteristics for improved matching.
• Predictive models assess pre-operative risks and post-operative complications (e.g., graft rejection, infections).
• AI-powered imaging aids in preoperative planning and real-time surgical guidance.

Main Results:

• AI can enhance the accuracy of transplant candidacy determination and organ allocation.
• Improved prediction of complications allows for timely, tailored interventions.
• AI-assisted surgery increases precision and safety, potentially improving long-term patient and graft survival.

Conclusions:

• AI demonstrates significant potential to revolutionize liver transplantation by improving decision-making, optimizing outcomes, and enhancing patient and graft survival.
• Key challenges include data availability and quality, ethical considerations, and clinical integration.