Advancements in Artificial Intelligence-Enhanced Imaging Diagnostics for the Management of Liver Disease-Applications and Challenges in Personalized Care

Abstract

Liver disease can significantly impact life expectancy, making early diagnosis and therapeutic intervention critical challenges in medical care. Imaging diagnostics play a crucial role in diagnosing and managing liver diseases. Recently, the application of artificial intelligence (AI) in medical imaging analysis has become indispensable in healthcare. AI, trained on vast datasets of medical images, has sometimes demonstrated diagnostic accuracy that surpasses that of human experts. AI-assisted imaging diagnostics are expected to contribute significantly to the standardization of diagnostic quality. Furthermore, AI has the potential to identify image features that are imperceptible to humans, thereby playing an essential role in clinical decision-making. This capability enables physicians to make more accurate diagnoses and develop effective treatment strategies, ultimately improving patient outcomes. Additionally, AI is anticipated to become a powerful tool in personalized medicine. By integrating individual patient imaging data with clinical information, AI can propose optimal plans for treatment, making it an essential component in the provision of the most appropriate care for each patient. Current reports highlight the advantages of AI in managing liver diseases. As AI technology continues to evolve, it is expected to advance personalized diagnostics and treatments and contribute to overall improvements in healthcare quality.

Keywords: artificial intelligence; deep learning; diagnosis; hepatocellular carcinoma; imagining; liver disease; personalized medicine; treatment.

Figure 1

The overview of AI models commonly used for training with image data. (a) Convolutional neural networks (CNNs) are specialized AI frameworks designed to process grid-like data structures such as images. They comprise convolutional layers to detect features through filters (kernels), pooling layers that reduce dimensionality and enhance computational efficiency, with non-linear activation functions to capture complex relationships, and fully connected layers for generating predictions. By combining these components, CNNs learn hierarchical representations and complex patterns. (b) Generative adversarial networks (GANs) are composed of two neural networks based on unsupervised (label-free) learning. The generator creates fake data by mapping random noise, which serves as the seed for the features of the generated data, to resemble the target data. The other network, the discriminator, is tasked with distinguishing between the fake data generated by the generator and real data, determining their authenticity. (c) Transfer learning applies knowledge from one task to another, enabling effective learning even with limited data. For example, when building classifiers for dogs and cats, a large dog image dataset may suffice for training, but a small cat dataset may limit accuracy. Transfer learning leverages a pre-trained model from a related domain, replacing and training only the output layer to adapt it to the new task. This approach enhances model accuracy even with minimal data and reduces the time for training.