Artificial intelligence-powered precision: Unveiling the landscape of liver disease diagnosis—A comprehensive review

Abstract

The global significance of diagnosing liver diseases is heightened, particularly in under-resourced regions with limited healthcare facilities. Traditional diagnostic methods, characterized by time and labor-intensive processes, have led to a growing demand for telemedicine-based solutions. The incorporation of Artificial Intelligence is deemed essential to enhance the efficiency and accuracy of diagnostic models. This review explores the seamless integration of diverse data modalities, including clinical records, demographics, laboratory values, biopsy specimens, and imaging data, emphasizing the importance of combining both types for comprehensive liver disease diagnosis. The study rigorously examines various approaches, focusing on pre-processing and feature engineering in non-image data diagnostic model development. Additionally, it analyzes studies employing Convolutional Neural Networks for cutting-edge solutions in image data interpretation. The paper provides insights into existing liver disease datasets, encompassing both image and non-image data, offering a comprehensive understanding of crucial research data sources. Emphasis is placed on performance evaluation metrics and their correlation in assessing diagnostic model efficiency. The review also explores open-source software tools dedicated to computer-aided liver analysis, enhancing exploration in liver disease diagnostics. Serving as a concise handbook, it caters to novice and experienced researchers alike, offering essential insights, summarizing the latest research, and providing a glimpse into emerging trends, challenges, and future trajectories in liver disease diagnosis.