Abstract: This paper introduces a system that harnesses the recommendations of generative artificial intelligence (AI) and more specifically, large language models (LLMs) to develop machine learning (ML) models for the automated detection of non-alcoholic fatty liver disease (NAFLD) on the basis of liver B-mode ultrasound images. The image dataset is minimal so the option of utilizing convolutional neural networks (CNNs) and deep learning (DL) approaches built around artificial neural networks and comparable systems is not pursued. Rather, experiments are carried out with simpler machine learning algorithms and classifiers such as random forest classifier, logistic regression and decision tree classifier. Results indicate reasonable performance in light of the fact that the utilization of CNNs and comparable DL approaches could lead to overfitting of the data. The generative AI is prompted with tailored prompts engineered to elicit recommendations that account for the characteristics of the dataset.
Keywords: Generative Artificial Intelligence (AI), Large Language Model (LLM), Convolutional Neural Network (CNN), Deep Learning (DL), Machine Learning (ML), Healthcare System, Disease Diagnosis and Prediction, Non-alcoholic Fatty Liver Disease (NAFLD).
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DOI:
10.17148/IJARCCE.2025.14301
