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The Helicobacter pylori AI-clinician harnesses artificial intelligence to personalise H. pylori treatment recommendations

Author

Listed:
  • Kyle Higgins

    (Imperial College London)

  • Olga P. Nyssen

    (Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD))

  • Joshua Southern

    (Imperial College London)

  • Ivan Laponogov

    (Imperial College London)

  • Dennis Veselkov

    (Imperial College London
    Imperial College London)

  • Javier P. Gisbert

    (Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD))

  • Tania Fleitas Kanonnikoff

    (Hospital Clínico Universitario de Valencia
    Instituto de Salud Carlos III)

  • Kirill Veselkov

    (Imperial College London
    Yale University)

Abstract

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.

Suggested Citation

  • Kyle Higgins & Olga P. Nyssen & Joshua Southern & Ivan Laponogov & Dennis Veselkov & Javier P. Gisbert & Tania Fleitas Kanonnikoff & Kirill Veselkov, 2025. "The Helicobacter pylori AI-clinician harnesses artificial intelligence to personalise H. pylori treatment recommendations," Nature Communications, Nature, vol. 16(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-61329-5
    DOI: 10.1038/s41467-025-61329-5
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    References listed on IDEAS

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    1. Fuchao Yu & Xianchao Xiu & Yunhui Li, 2022. "A Survey on Deep Transfer Learning and Beyond," Mathematics, MDPI, vol. 10(19), pages 1-27, October.
    2. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
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