The Helicobacter pylori AI-clinician harnesses artificial intelligence to personalise H. pylori treatment recommendations
- Kyle Higgins
- Olga P. Nyssen
- Joshua Southern
- Ivan Laponogov
- Ana Miralles Marco
- Manuel Cabeza-Segura
- Elena Jiménez Martí
- Josefa Castillo
- Mārcis Leja
- Inese Poļaka
- Fatima Carneiro
- Ceu Figueiredo
- Rui M. Ferreira
- Rita Barros
- Leticia Moreira
- Miriam Cuatrecasas
- Gloria Fernandez-Esparrach
- Tamara Matysiak-Budnik
- Jerome Martin
- Laimas Jonaitis
- Juozas Kupčinskas
- Paulius Jonaitis
- Mário Dinis-Ribeiro
- Miguel Coimbra
- Ana Carina Pereira
- Filipa Fontes
- Manon C. W. Spaander
- Judith Honing
- Stefano Sedola
- Junior Andrea Pescino
- Zorana Maravic
- Ana Martins
- Dennis Veselkov
- Javier P. Gisbert
- Tania Fleitas Kanonnikoff
- Kirill Veselkov
2025-07-14
Helicobacter pylori ( H. pylori ) is the most common carcinogenic pathogen globally and the leading cause of gastric cancer. Here, we develop a reinforcement learning-based AI Clinician system to personalise treatment selection and evaluate its ability to improve eradication success compared to clinician-prescribed therapies. The model is trained and internally validated on 38,049 patients from the retrospective European Registry on Helicobacter pylori Management (Hp-EuReg), using independent state deep Q-learning (isDQN) to recommend optimal therapies based on patient characteristics such as age, sex, antibiotic allergies, country, and pre-treatment indication. In internal validation using real-world Hp-EuReg data, AI-recommended therapies achieve a 94.1% success rate (95% CI: 93.2–95.0%) versus 88.1% (95% CI: 87.7–88.4%) for clinician-prescribed therapies not aligned with AI suggestions—an improvement of 6.0%. Results are replicated in an external validation cohort ( n = 7186), confirming generalisability. The AI system identifies optimal treatment strategies in key subgroups: 65% ( n = 24,923) are recommended bismuth-based therapies, and 15% ( n = 5898) non-bismuth quadruple therapies. Random forest modelling identifies region and concurrent medications as patient-specific drivers of AI recommendations. With nearly half the global population likely to contract H. pylori , this approach lays the foundation for future prospective clinical validation and shows the potential of AI to support clinical decision-making, enhance outcomes, and reduce gastric cancer burden.