Generative Artificial Intelligence-Aided Clinical Measurement-Based Liver Disease Diagnosis

Abstract

This paper presents a system that generates artificial intelligence (AI) models for the automated diagnosis of liver disease (cirrhosis of the liver) based on the recommended of generative AI tools such as large language models (LLMs). System architectures suggested by the LLMs via prompt engineering are implemented using the TensorFlow framework and trained, tested and validated on publicly accessible liver disease datasets comprising clinical or diagnostic measurements of factors such as age, gender, total bilirubin, direct bilirubin, total proteins, albumin, albumin and globulin ratio, alanine aminotransferase, aspartate aminotransferase and alkaline phosphatase. After fine-tuning for robustness and enhanced performance, the resulting AI models could be harnessed into modules for the automated diagnosis of liver disease within the framework of a comprehensive AI-driven healthcare system.