The world of artificial intelligence (AI) is rapidly advancing, but its impact on childhood cancer care is a unique and critical area of focus. While AI has the potential to revolutionize cancer treatment, the unique challenges and vulnerabilities of children with cancer demand a thoughtful and nuanced approach. In this article, I will explore the human side of AI in childhood cancer and argue that it is not just about the technology, but also about the people and the relationships it impacts. I will delve into the ethical considerations, the impact on families, and the potential for AI to augment, rather than replace, human care. Finally, I will discuss the importance of embedding multi-centre, child-specific evaluation and designing models and interfaces with families at the table to ensure that AI in childhood cancer is used in a way that honors children's vulnerability and potential.
The Unique Challenges of Childhood Cancer
Childhood cancer is different from adult cancer in many ways. Treatments developed for adult bodies cannot simply be scaled down to child-size, and there are no obvious behavioural preventions for children. In Australia, childhood cancers are rare, accounting for well under one percent of all new cancer diagnoses each year. This rarity makes it difficult to assemble large, balanced datasets, which is one of the implicit promises of AI: that more data will always smooth out the rough edges.
However, in the context of childhood cancer, the numbers are small, the futures are long, and every decision reverberates through a family's life. This makes childhood cancer an uncomfortable fit for AI models built on 'big data' and narrow performance metrics. It also raises a deeper question: what would it mean for AI to make care more human, rather than less?
The Impact on Families
Childhood cancer survivors live for decades with the consequences of our choices. Radiotherapy fields, chemotherapy doses, and surgical decisions shape not only survival but cognitive function, fertility, employment, and independence well into adulthood. AI systems can already outline tumours on MRI scans, distinguish between some tumour types, and even hint at underlying molecular changes that guide treatment. However, the real significance of these tools is not only in what they can see in the pixels, but in how they force us to renegotiate relationships between clinicians, families, systems, and children who cannot easily speak for themselves.
Families will rightly want to know: who owns those decisions? How do we weigh a small gain in predicted disease control against a higher risk of learning difficulties or secondary malignancy 20 years later? No algorithm can answer that; it can only provide another layer of information for clinicians, parents, and – where possible – the child to interpret together.
The Importance of Transparency and Humility
Data scarcity in paediatrics makes transparency and humility non-negotiable features of any ethical AI. Families need to know how many children like theirs the model has actually seen and how often it has been wrong. This is not just an implementation delay, but a risk to trust. Building trust in this setting means more than explaining how a computer vision system works; it means honest conversations about uncertainty, clear lines of responsibility when the AI and the clinician disagree, and involvement of parents and young people in deciding how their data are used to train future systems.
The Potential for Augmentation
AI can take over hours of manual tumour measuring and outlining, giving radiation oncologists and radiologists back precious time. Models designed around the messy reality of clinical practice – missing MRI sequences, inconsistent scan quality – show that thoughtful engineering can bridge lab and bedside. Biopsy-scanning algorithms can pre-screen slides, allowing pathologists to focus on the most ambiguous regions.
The human question is what we do with the time and mental bandwidth this frees. Augmentation worth having would let clinicians spend less energy chasing images, formatting reports, and drawing tumour outlines – and more energy sitting with parents at diagnosis, checking in on siblings, coordinating school reintegration, or discussing fertility preservation.
The Importance of Children's Perspective
Most commentary on AI in health treats paediatrics as an afterthought. Yet there is a strong argument for the opposite: that children with cancer should be at the centre of how we design and govern AI. They expose the weaknesses of 'big data solves everything' thinking, remind us that some patients are structurally underrepresented in datasets, and that justice requires deliberate over-representation of their needs when building systems that will shape care.
Most of all, they insist that care is relational. An algorithm may be state-of-the-art in reading MRI scans, but if using it means less continuity with a trusted nurse, more fragmented appointments, or fewer opportunities for a teenager to voice fears about relapse, its net effect on 'care' may be negative.
The Way Forward
If we can get AI right in paediatric oncology – small numbers, high stakes, lifelong consequences – we will have gone a long way towards getting it right elsewhere. This would mean embedding multi-centre, child-specific evaluation as the norm, not the exception, before widespread deployment; designing models and interfaces with families at the table, not merely as data sources; building governance that treats AI decisions as joint human-machine judgements, with clear accountability when things go wrong; and measuring success not only in test scores, but in time returned to relationships, reduced distress, and better long-term outcomes that matter to survivors.
AI in paediatric cancer will continue to evolve: better imaging analysis, richer scan pattern insights, and smarter handling of missing data. The technology curve is steep. The question is whether the human curve – our capacity to use these tools in ways that honour children's vulnerability and potential – can keep pace. If we allow childhood cancer to be our ethical compass, it may yet guide AI in health towards a future that is not just more intelligent, but more humane.
