Accessing Artificial Intelligence for Clinical Decision-Making - Giordano, Chris; Brennan, Meghan; Mohamed, Basma; Rashidi, Parisa; Modave, François; Tighe, Patrick (Frontiers in Digital Health, 2021)
The near universal acceptance and implementation of electronic medical records (EMRs) has provided an unimaginable amount of data that has paved the way for personalized, automated, and immediate patient care models that were not previously possible. Artificial intelligence (AI) methods and its subfields of machine learning (ML), reinforcement learning, and deep learning are well-suited to deal with such data. This appropriately cultivated and curated data can assist decision-makers stratify preoperative patients into risk categories, as well as categorize the severity of ailments and health for nonoperative patients admitted to hospitals. Such stratification can help access and deploy correct resources and match value-based care with patient condition. AI will help overcome challenges with multiple outcome optimization limitations or sequential decision-making protocols that limit individualized patient care. The previous overt, traditional vital signs and laboratory values that were used to signal alarms for an acutely decompensating patient will be replaced by continuously monitoring and updating AI tools that can pick up early imperceptible patterns predicting subtle health deterioration. Such a tool will permit early, less-invasive interventions to restore wellbeing, thereby improving patient care models. However, the data sets that AI models train on and develop have the potential for misapplication and thereby create concerns for application bias. Subsequently, the mechanisms governing this disruptive innovation must be understood by clinical decision-makers to prevent unnecessary harm. This need will force physicians to change their educational infrastructure to facilitate understanding AI platforms, modeling, and limitations to best acclimate practice in the age of AI.