Loading
Life sciences · Journal article
Frontiers in Medicine · October 16, 2026
No summary has been generated for this record yet. What follows is drawn from its source metadata only.
Journal article.
No findings were extractable from the material analysed.
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
The source did not state who this applies to in practice.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
This record has not been graded across any dimension yet. Treat the label above as provisional and read the source.
What is missing. This record has no bottom line, key findings, reported figures, evidence dimensions. That is a gap in the analysis, not a judgement about the study.
Pediatric cancer immunotherapy has expanded from anti-GD2 antibodies and bispecific T-cell engagers to immune checkpoint inhibitors and chimeric antigen receptor T-cell therapy. These treatments can produce rapid, nonlinear, and partially overlapping toxicities, including cytokine release syndrome, immune effector cell-associated neurotoxicity syndrome, infection, prolonged cytopenia, B-cell aplasia, and organ-specific immune-related adverse events. Nurses provide the highest-frequency clinical observations across inpatient, ambulatory, and home settings, yet conventional monitoring often fragments laboratory data, vital signs, neurologic findings, caregiver observations, and patient-reported outcomes. Artificial intelligence (AI) offers a means to fuse these longitudinal signals into individualized risk trajectories. The clinically relevant mechanism, however, is not autonomous prediction; it is a nurse-in-the-loop system in which AI improves temporal sensitivity and prioritization, while nurses verify signal quality, interpret age- and development-specific context, reconcile child–caregiver discordance, activate protocol-based escalation, and maintain accountability. This Mini Review examines how biomarkers, continuous physiological monitoring, electronic patient-reported outcomes, and caregiver-reported behavioral changes can support AI-enabled nursing surveillance during acute immunotoxicity and long-term survivorship. We propose a three-layer framework linking immune-toxicity biology, a multimodal digital phenotype, and nursing counter-regulation. We also discuss workflow integration, alert fatigue, model drift, bias, privacy, and the need for prospective pediatric validation. AI should augment not replace specialist pediatric oncology nursing judgment and should be evaluated by whether it improves timely intervention, symptom burden, family experience, and long-term safety.