AI in Pediatric Oncology
Artificial intelligence as a decision-support tool alongside the pediatric oncology team.
Artificial intelligence (AI)—including machine learning, deep learning and large-language models—is emerging as an important tool across the pediatric oncology pathway.
AI can help clinicians analyse complex and high-dimensional data from imaging, digital pathology, genomics, electronic health records and treatment response, potentially improving diagnostic accuracy, risk stratification, outcome prediction and treatment selection.
In rare pediatric cancers, where individual centres often have limited datasets, AI may also enable meaningful pattern recognition and collaborative analysis across large multicentre datasets.
Emerging applications
Radiomics
Automated pathology interpretation
Molecular classification
Prediction of treatment response and toxicity
Clinical decision support
Identification of potential therapeutic opportunities
Unique challenges in pediatric oncology
Pediatric oncology presents challenges that general-purpose AI work does not always account for:
- Small and heterogeneous datasets
- Limited external validation
- Algorithmic bias
- Interpretability
- Data privacy
- The need for prospective clinical validation
The guiding principle
AI should be viewed as a decision-support and knowledge-enhancement tool that works alongside the pediatric oncology team, with clinical decisions remaining under expert human oversight.
Further reading
“The future of pediatric oncology will not be AI versus the clinician—it will be clinicians empowered by AI, molecular data and collaborative intelligence to make better decisions for every child.”