Summary: This article reviews AI enabled symptom monitoring for oncology nursing with a clinical and translational perspective. It synthesizes PRO integration, predictive toxicity models, and nurse led intervention pathways.

Cancer treatment causes dynamic symptom burdens requiring timely nursing interventions. AI can synthesize PROs, vitals, and labs to predict severe toxicity and guide proactive nursing outreach.

Technically models use PRO time series, lab trends, and treatment regimens to predict neutropenia, dehydration, and uncontrolled pain. NLP extracts symptom descriptions from notes. Validation requires oncology specific cohorts, prospective trials, and linkage to clinical outcomes such as ED visits and treatment adherence.

Guidance: Guidance: co design alerts with oncology nurses, prioritize high precision for actionable outreach, integrate with infusion center workflows, and include patient education and escalation criteria.

Conclusion: AI symptom systems can reduce adverse events and improve patient experience when oncology specific data and nurse workflows are central to design.

Final Summary: PRO integration; toxicity prediction; infusion workflow integration; nurse outreach; patient education

Useful Facts: oncology | symptom management | nursing care

Related Topics: nursing;oncology PROs predict deterioration; NLP captures nuanced symptom reports; High precision alerts reduce unnecessary calls; Integration with infusion centers improves timeliness; Oncology validation is essential