Chairs:
Dr Olena Gruzieva (Stockholm, Sweden), Dr David Lo (Leicester, United Kingdom)
Speakers:
Dr Amy Hai Yan Chan (Auckland, New Zealand), Prof. Amelia Licari (Pavia, Italy)
Fees:
Free for ERS members / €10 for non-members
Overview:
Artificial intelligence (AI) and machine learning (ML) are rapidly transforming healthcare, but there is a critical educational gap regarding how these technologies translate into tangible clinical benefits.
Specifically, in paediatric asthma, a heterogeneous condition, AI offers two distinct but complementary opportunities:
1) enhancing diagnostic precision through advanced imaging analysis (e.g., phenotyping severe asthma via HRCT)
2) revolutionising daily care through predictive modelling and supported self-management (e.g., smart inhalers and digital biomarkers).
This webinar aims to promote discussion and education around how the gap between data science and clinical practice can be bridged; providing examples of how AI/ML tools can be used to detect structural airway changes earlier and predict exacerbations before they occur, ultimately reducing the burden of disease.
Topics:
- AI in Diagnostic Imaging: utilising machine learning to detect structural airway changes and phenotype severe asthma (HRCT analysis).
- Digital Biomarkers & Prediction: harnessing data from smart devices (inhalers, wearables) to predict asthma attacks.
- Supported Self-Management: the role of AI-driven personalised feedback in improving medication adherence in children.
- From Code to Clinic: overcoming barriers to implementing AI tools in routine paediatric respiratory care.
Format:
One-hour webinar structured as follows:
- Opening remarks - Introduction by Chairs - Olena Gruzieva, David Lo (4 min)
- Presentation 1: AI Opportunities for Supported Asthma Self-Management - Amy Hai Yan Chan (20 min)
- Presentation 2: Machine Learning-Enhanced Imaging in Paediatric Asthma - Amelia Licari (20 min)
- Live Q&A and round table - moderated by Chair (15 min)
- Wrap-up (1 min)
- Learning outcomes
Following this webinar, participants will be able to:
- Describe the current and emerging applications of AI/ML in paediatric respiratory diagnostics and management.
- Evaluate the utility of ML-enhanced imaging for identifying severe asthma phenotypes compared to traditional methods.
- Interpret how real-time data from digital devices can be processed by AI to predict exacerbations and support patient self-management.
- Recognise the potential pitfalls, ethical considerations, and clinical workflow challenges of integrating AI into daily practice.
Please note this webinar is not hosted by ARTP. Please contact e-learning@ersnet.org for more info