Artificial intelligence and pediatric imaging data: ethical strategies for learning and collaboration.
Authors
Affiliations (8)
Affiliations (8)
- Artificial Intelligence and Translational Imaging (ATI) Lab, Department of Radiology, School of Medicine, University of Crete, 70013, Heraklion, Greece.
- Computational Biomedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology Hellas (ICS-FORTH), Heraklion, Greece.
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
- Department of Medical Imaging, University Hospital of Heraklion, Heraklion, Greece.
- Artificial Intelligence and Translational Imaging (ATI) Lab, Department of Radiology, School of Medicine, University of Crete, 70013, Heraklion, Greece. [email protected].
- Department of Medical Imaging, University Hospital of Heraklion, Heraklion, Greece. [email protected].
- Computational Biomedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology Hellas (ICS-FORTH), Heraklion, Greece. [email protected].
- Division of Radiology, Department of Clinical Science Intervention and Technology (CLINTEC), Karolinska Institute, Heraklion, Greece. [email protected].
Abstract
The integration of artificial intelligence (AI) in pediatric radiology requires an interdisciplinary approach that prioritizes transparency, accountability and collaboration between developers, clinicians and regulatory bodies. The development of AI models that are specifically designed to analyze pediatric imaging data has the potential to improve diagnosis and treatment outcomes, but it also requires careful consideration of the ethical implications. This review highlights the importance of the unique challenges posed by AI in pediatric imaging data, including regulatory hurdles, bias mitigation and the need for human oversight. Facing this situation, pediatric radiologists need to be equipped with the skills and knowledge to critically evaluate AI outputs and address potential biases and limitations. This requires ongoing education and training in pediatric radiology as well as AI. The integration of AI in pediatric radiology requires a collaborative approach that involves not only developers and clinicians but also patients and families. Ultimately, the integration of AI in pediatric imaging needs to be a coordinated effort from all stakeholders to prioritize the long-term safety and health of the young patients.