Artificial Intelligence in Clinical Applications of Helicobacter pylori: A Review

Helicobacter pylori infection remains a globally prevalent condition linked to peptic ulcers and gastric cancer, necessitating timely and accurate diagnosis. This review analyses a set of relevant studies that apply artificial intelligence methods to the detection and management of H. pylori. For each study, we examined key aspects including the AI technique employed, the type of input data used, reported performance, and stated limitations. We found that most approaches focused on early detection, particularly through medical imaging and deep learning models. While many systems achieved accuracy comparable to that of clinical experts, limitations were frequent, including small sample sizes, lack of external validation, and reduced effectiveness in post-eradication cases. Only a minority of works addressed explainability or assessed model performance across diverse populations. These findings reveal both the promise and the current barriers of AI-based tools for H. pylori, emphasizing the need for more robust, generalizable, and interpretable solutions.