Integrating artificial intelligence into onco-critical care practice

Abstract

Artificial intelligence (AI) is witnessing an evolutionary change onco-critical care practice due to its ability to guide clinical decisions on the critically ill cancer patient population, enabling the delivery of precise, data-driven, and proactive care. It is a distinctly complicated area because it incorporates the pathophysiology of malignancy and acute organ dysfunction, the toxicity caused by the treatment and the infectious or inflammatory complications. Although useful, traditional scoring instruments and clinical assessment devices are constrained in the ability to represent nonlinear and dynamic interplay that is inherent in this population. State-of-art AI techniques and models, such as machine learning (ML), deep learning (DL), and natural language processing (NLP), can provide remedies when they generate high-dimensional multimodal data using electronic health records, medical imaging, physiological waveforms, laboratory metrics, and molecular profiles. Recent data prove that AI models can result in a higher rate of timely oncologic crisis, clinical prognostication, clinical worsening, and individual treatment of the patient in the intensive care unit. Clinical decision support systems that are powered by AI maximize real-time risk, support antimicrobial and anticancer therapy, and ventilatory and hemodynamic support and reduce clinician mental load and care variation. Multimodal predictive models that combine both structured and unstructured data also enable prognostic forecasting of symptoms and resource optimization to enhance patient outcomes and the ICU efficiency. However, there are still challenges such as data quality, interpretability, ethical concerns and integration into clinical processes. The safe implementation requires addressing the issues of algorithmic bias, guaranteeing transparency, and keeping clinicians supervising the implementation. Together with the clinical experience, AI promises to be a powerful augmentative intervention in onco-critical care, fulfilling contemporary high-value, timely, and personalized care to the most acute oncology patients.

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