Letter to the Editor: Artificial intelligence in hepatology - when deep learning meets drug-induced liver injury

Abstract

Drug-induced liver injury (DILI) remains one of the most challenging diagnoses in hepatology due to its complexity, reliance on expert interpretation and exclusion criteria, and the frequent need for invasive procedures. A notable example is pyrrolizidine alkaloid-induced hepatic sinusoidal obstruction syndrome, where identification is often delayed because of nonspecific clinical symptoms and subtle imaging findings. In this context, the work published in World Journal of Gastroenterology by Wang et al, introduces a deep learning model for computed tomography that could significantly change the approach to treating DILI. This model is designed to complement physicians’ judgment rather than replace it. It aligns with anatomical reasoning, captures multiscale parenchymal anomalies, and demonstrates robust and consistent performance across multiple centers. Crucially, the use of this model has led to a notable improvement in diagnostic accuracy among junior doctors and has reduced the time needed for interpretation, all without sacrificing specialist-level performance, without sacrificing specialist-level performance, model aid greatly increased junior doctors’ diagnostic accuracy and decreased interpretation time. These results highlight artificial intelligence as a tool to equalize diagnostic capabilities, standardize knowledge, expedite decision-making, and potentially reduce reliance on invasive testing. Integrating such artificial intelligence techniques into the workflow could revolutionize the identification of rare and complex forms of DILI in routine practice as hepatology advances toward precision diagnostics.

Keywords: Drug-induced liver injury; Pyrrolizidine alkaloid; Sinusoidal obstruction syndrome; Deep learning; Computed tomography model

Core Tip: Drug-induced liver injury remains challenging due to its vague presentation and reliance on exclusion-based techniques. This complexity is particularly evident in cases of pyrrolizidine alkaloid-induced hepatic sinusoidal obstruction syndrome, which often require invasive treatments and expert imaging interpretation. Recent advancements in deep learning applied to computed tomography are changing the diagnostic landscape by identifying subtle, diffuse parenchymal abnormalities that might be missed using standard methods. Artificial intelligence can improve clinician performance, enhance diagnostic consistency, and reduce interpretation times, as demonstrated by the validated model discussed here. These integrated technologies could improve the early identification of complex drug-induced liver injury characteristics.