Artificial Intelligence in Oncology Nursing

A rtificial intelligence (AI), including machine learning, predictive analytics, natural language processing, computer vision, and generative AI, is increasingly used across the cancer continuum.In oncology nursing, AI may improve early identification of patient needs, personalize supportive care, reduce administrative burden, and expand access to expertise.However, the evidence remains limited.Many applications have not been rigorously evaluated for real-world clinical effectiveness, nursing-sensitive outcomes, workflow impacts, or unintended consequences (O'Connor et al., 2024;Zhou et al., 2024).Cancer care requires critical reasoning, ethical judgment, therapeutic communication, and individualized decisions that cannot be delegated to technology.Oncology nurses must retain authority to question, escalate, report, and override unsafe and inequitable AI recommendations to ensure highquality patient care.AI systems may produce inaccurate outputs, perform inconsistently across populations and settings, threaten privacy, and amplify inequities (Chen et al., 2023;Hantel et al., 2024;Higgins et al., 2024;Istasy et al., 2022).Therefore, meaningful human oversight, transparent governance, local validation, continuous monitoring, and clear accountability are essential in AI development and utilization.Equitable implementation requires careful attention to the representativeness of AI datasets, the distribution of access to AI-enabled care, the valuation of human expertise, and the allocation of risk when these technologies fail.Underrepresentation may introduce structural inequities and exacerbate existing disparities in cancer care and outcomes (Dankwa-Mullan & Weeraratne, 2022;Miyawaki et al., 2025).Oncology nurses must be leaders in AI design, procurement, implementation, governance, research, and policy.This leadership includes