Current applications of artificial intelligence and machine learning in oncologic ICU management

Abstract

Critically ill cancer patients have challenges in management owing to their immuno compromised state, complex comorbidities, and heightened risk of multi-organ dysfunction. Recent advances in artificial intelligence and machine learning are revolutionizing oncologic intensive care through the facilitation of early complication prediction, precision risk stratification, and real-time decision support. These technologies can integrate large datasets, including clinical parameters, laboratory results, imaging findings, and genomics, for anticipating events such as sepsis, acute respiratory distress syndrome, and cytokine release syndrome. AI-driven algorithms will also facilitate optimization of ventilatory strategies, hemodynamic management, and renal support so that individualized support can be offered to each patient profile. While promising, implementation calls for careful validation, ethical oversight, and integration into multidisciplinary care pathways. This review elaborates on current applications, emerging innovations, and future perspectives of AI and ML in improving outcomes for critically ill patients with cancer.

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