Table 2.

Advantages and Challenges of AI Implementation to Enhance CRRT Delivery

Advantages	Challenges
Automated learning from multimodal data (CRRT machine, monitors, EHRs, etc.) supersedes human accuracy in characterizing disease pathophysiology and predicting clinical outcomes	Inherent algorithmic bias may compound in a vicious cycle, misleading clinical decisions that ultimately result in worse outcomes. Standardized metrics and policy to evaluate utility, implementation, and sustainability of AI-based tools are needed
Inclusive databases and fair algorithmic models can identify marginalized populations, reduce health service disparity, and optimize treatment benefit based on complex sociodemographic, resource allocation and access, and clinical characteristics	Propensity to exacerbate healthcare inequities given that CRRT is mostly available in high and middle-income countries. Databases must be checked for diversity and algorithms for structural inequities and biases
Optimized allocation of resources such as CRRT machines based on specific patient needs and resource availability	Alert/monitoring fatigue nullifies or even reverses benefit of early risk identification/allocation, as true clinical deterioration could be ignored in a stream of low priority alerts
Automated clinical and quality assurance workflow streamlines effective CRRT service delivery across a health system	Implementation and deployment may become a burden in small size hospitals and/or low-income countries