Artificial Intelligence and Machine Learning in Drug Design and Development

16Empowering Clinical Decision Making: An In-Depth Systematic Review of AI-Driven Scoring Approaches for Liver Transplantation Prediction

Devi Rajeev¹, Remya S.¹ and Anand Nayyar²*

¹School of Computing, Amrita Vishwa Vidyapeetham, Amritapuri, Kerala, India

²School of Computer Science, Duy Tan University, Da Nang, Viet Nam

Abstract

Effective clinical decision-making is critical in liver transplantation, as timely and precise assessments substantially influence patient outcomes. The chapter examine the possible benefits of artificial intelligence (AI) tools in improving clinical decision-making in liver transplantation. As part of this research, 44 relevant research papers are analyzed that satisfied the inclusion requirements. Various AI methodologies in liver transplantation, such as machine learning, deep learning, and predictive modeling are examined. This study aimed to assess whether AI-based scoring algorithms can improve the accuracy and efficiency of clinical judgments and predict outcomes such as graft failure, patient survival, and rejection. The findings suggest that AI-based models can improve clinical decision-making by providing accurate forecasts of critical outcomes and expediting evaluations, resulting in timely interventions. However, successfully integrating AI into clinical practice requires further research and validation. These insights benefit doctors, researchers, and policymakers interested in leveraging AI to enhance decision-making efficiency in liver ...