A narrative review of progress in the application of artificial intelligence in acute respiratory distress syndrome: subtypes and predictive models

BACKGROUND AND OBJECTIVE: Acute respiratory distress syndrome (ARDS) occurs in different populations, and it is very challenging to manage heterogeneous patient groups. Artificial intelligence (AI) aids in interpreting complex data of patients with ARDS and can be used to detect adverse events as it can automatically capture complex relationships. This review aimed to explore the application and progress of AI in ARDS (e.g., subgroup classification of patients with ARDS via unsupervised clustering and supervised predictive models for early detection) and identify the current ARDS-related problems that can be solved using AI. METHODS: This comprehensive and narrative review was performed to obtain information about the application of AI in ARDS and summarize its subtypes and predictive models. KEY CONTENT AND FINDINGS: The current applications of AI and machine learning in ARDS include ARDS subgroup classification, diagnosis, and survival prediction. In this review, the current problems that should be addressed by AI in ARDS were identified, and our findings may serve as a useful reference for its translational use in the ARDS field. CONCLUSIONS: Owing to the discovery of hyper- and hypoinflammatory subtypes, individualized treatment of ARDS is possible, and diagnosis and survival prediction are essential in disease management and planning. However, prospective studies should clarify the reliability and generalizability of the results using AI and machine learning and performing bedside testing in larger populations to establish a more stable and time-resilient model. Therefore, a consensus on conducting and reporting machine learning studies in medicine should be urgently established.