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Background and aim
Artificial intelligence (AI)-powered chatbots, such as Chat Generative Pretrained Transformer (ChatGPT), have shown promising results in healthcare settings. These tools can help patients obtain real-time responses to queries, ensuring immediate access to relevant information. The study aimed to explore the potential use of ChatG...
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Citations
... These conversational tools, such as ChatGPT, are trained on large language datasets and can generate new content by identifying and replicating patterns from their training data [17,38]. ChatGPT, for example, is based on OpenAI's Generative Pretrained Transformer (GPT) model and has demonstrated its effectiveness in providing answers to a wide range of queries in a variety of healthcare settings, including mental health support and chronic disease management [17,39]. Despite their potential, LLMs also raise concerns, especially around privacy, the adequacy of their training, and the reliability of their output [17,38]. ...
... Several studies have investigated the performance of ChatGPT in responding to MASLD-related queries [23,24,26,39]. A study involving ten key opinion leaders in the field of MASLD evaluated ChatGPT 3.5's responses to patient queries in English, focusing on accuracy, completeness, and comprehensibility, using three-and six-point Likert scales. ...
Metabolic dysfunction-associated steatotic liver disease (MASLD) is emerging as a leading cause of chronic liver disease. In recent years, artificial intelligence (AI) has attracted significant attention in healthcare, particularly in diagnostics, patient management, and drug development, demonstrating immense potential for application and implementation. In the field of MASLD, substantial research has explored the application of AI in various areas, including patient counseling, improved patient stratification, enhanced diagnostic accuracy, drug development, and prognosis prediction. However, the integration of AI in hepatology is not without challenges. Key issues include data management and privacy, algorithmic bias, and the risk of AI-generated inaccuracies, commonly referred to as “hallucinations”. This review aims to provide a comprehensive overview of the applications of AI in hepatology, with a focus on MASLD, highlighting both its transformative potential and its inherent limitations.
Artificial intelligence (AI) methods enable humans to analyse large amounts of data, which would otherwise not be feasibly quantifiable. This is especially true for unstructured visual and textual data, which can contain invaluable insights into disease. The hepatology research landscape is complex and has generated large amounts of data to be mined. Many open questions can potentially be addressed with existing data through AI methods. However, the field of AI is sometimes obscured by hype cycles and imprecise terminologies. This can conceal the fact that numerous hepatology research groups already use AI methods in their scientific studies. In this review article, we aim to assess the contemporaneous use of AI methods in hepatology in Europe. To achieve this, we systematically surveyed all scientific contributions presented at the EASL Congress 2024. Out of 1,857 accepted abstracts (1,712 posters and 145 oral presentations), 6 presentations (∼4%) and 69 posters (∼4%) utilised AI methods. Of these, 55 posters were included in this review, while the others were excluded due to missing posters or incomplete methodologies. Finally, we summarise current academic trends in the use of AI methods and outline future directions, providing guidance for scientific stakeholders in the field of hepatology.
