Facilitating the Next Paradigm Shift in Critical Care Through Artificial Intelligence

Rishikesan Kamaleswaran, PhD, EditorView Large Image Figure ViewerDownload Hi-res image Download (PPT)From the mechanical ventilator to extracorporeal circulatory support, every few years those who facilitate, practice, or study critical care are introduced to new technologies that redefine the status quo and introduce new challenges and possibilities. Machines that can exhibit artificial intelligence (AI), defined as the ability of computer systems to mirror behaviors once thought unique to humans (such as pattern recognition and decision making), may be the next major change seen in the study and practice of critical care. The field of Machine Learning (ML), how machines utilize AI using statistical and mathematical constructs, is evolving as quickly as the capacity of modern computing. While statistics is foundational to these new approaches, especially in the context of supervised learning (training a system to recognize or predict an outcome defined within the data), other areas of ML, such as unsupervised learning (training a system to recognize new, previously unseen patterns in data), deep learning (through the use of “neural nets,” modeled after the human brain), and computer vision, have made this field uniquely suited to tackle many challenges of contemporary critical care. The rapid pace of advancements in this area has made it difficult to predict even how the near term may present itself, yet what is clear is the transformational change that will be felt across all facets of human knowledge, including the critical care field in general. This collection is designed to provide an overview of the promise and potential challenges of AI in the practice, oversight, and study of critical care medicine. The first few articles discuss how AI might directly impact bedside clinicians of the future. Dr Sanchez-Pinto and colleagues introduce the potent methods that exist in reducing heterogeneity across critical care and improving treatment effectiveness through precision medicine approaches. Dr Liu and colleagues present the broad area of AI/ML applied to the electronic health record (EHR) and highlight the key advantages and challenges in the domain. Dr Kim and colleagues introduce novel data sets and modalities that may provide incredible insights into physiologic dynamics of critical illness. Dr Clermont introduces how the EHR may be used within the context of an interactive learning framework through advanced interoperable tools and standards. The next article reviews how AI can improve deployment of critical care. Dr King and colleagues discuss how data science may be a powerful tool to advance implementation science, moving from population-based methods to incorporating individualized approaches. Next, we have two articles that discuss the implications of AI on the execution of critical care research. Dr Horvat reviews how Bayesian analysis can increase the efficiency and effectiveness of clinical trials, with lessons learned from the Randomized, Embedded, Multifactorial Adaptive Platform for Community-Acquired Pneumonia platform. Dr Agarwal and colleagues shed light on how the EHR can streamline clinical trial recruitment. The collection then talks about the challenges faced by the use of AI systems in the critical care landscape. Dr Wardi and colleagues give a very comprehensive overview of the different techniques applied to predictive models that make them more generalizable, especially for heterogenous syndromes like sepsis and acute respiratory distress syndrome. Dr Rojas and colleagues provide an excellent synopsis of why the right amount of clinician trust in AI is needed for appropriate use in the critically ill, and the systemic changes necessary to reach that goal. Finally, the collection closes with two bioethical dilemmas of using AI in critical care medicine. Dr Caruso and colleagues provide a balanced discussion of the possible benefits and costs of AI in critical care, indicating that the potential benefits of continuous surveillance of patients and decreased use of hospital resources may outweigh the upfront costs to infrastructure and workflow changes. Dr Charpignon and colleagues highlight the potential of AI to perpetuate inequitable care through biases in measurement, calibration, and patient selection that adversely affect segments of the population and provide possible solutions to mitigate these problems in future algorithms. Our goal is to provide a roadmap for effective integration of AI into critical care systems, practice, and research. We hope that this collection will help revolutionize critical care to improve outcomes of those who need it.