Artificial intelligence for early sepsis detection and dynamic prognostication in onco-critical care

Table 4 Key barriers and proposed solutions for the clinical implementation of artificial intelligence in the oncology intensive care unit

Implementation barrier	Description and clinical impact	Proposed solutions and emerging strategies
The “black box” phenomenon	Deep learning and complex ensemble models lack transparency, making clinicians hesitant to trust or act upon life-altering predictions without understanding the underlying reasoning[3,9]	Implementation of explainable AI tools (e.g., SHAP, LIME) to visualize feature importance; use of intrinsically transparent models like Bayesian Networks[24,35]
Data heterogeneity and domain shift	Models trained on general ICU populations (e.g., MIMIC databases) often perform poorly on highly specific subgroups like hematologic malignancy patients due to physiological differences[3,7]	Development of diverse, multi-center datasets; specific local recalibration of models; utilizing federated learning to train models across institutions while preserving data privacy[3,24]
Alert fatigue and workflow disruption	Overly sensitive algorithms generate excessive false-positive alarms, overwhelming staff and leading clinicians to ultimately ignore the system[9,11]	Calibrating models for higher specificity and positive predictive value; embedding alerts seamlessly into the EHR workflow; human-in-the-loop system designs[9,21]
Class imbalance in datasets	In hospital datasets, the number of patients dying from sepsis is significantly lower than the number of survivors, causing ML models to become biased toward predicting survival[17,39]	Utilizing data preprocessing techniques such as the synthetic minority over-sampling technique to synthetically balance the dataset during the training phase[17,39]
Lack of prospective validation	The vast majority of current models are based on retrospective observational data, lacking the rigorous evidence required to prove they safely improve patient outcomes[9,18]	Conducting large-scale, multi-center randomized controlled trials specifically assessing the safety, efficacy, and ethical integration of AI-driven clinical decision support systems[9,21]

SHAP: SHapley Additive exPlanations; LIME: Local interpretable model-agnostic explanations; ICU: Intensive care unit; EHR: Electronic health record; ML: Machine learning.