• João Matos, Jack Gallifant, Anand Chowdhury, Nicoleta Economou-Zavlanos, Marie-Laure Charpignon, Judy Gichoya, Leo Anthony Celi, Lama Nazer, Heather King, and WongAn-Kwok IanAIDivision of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Duke University, Durham, NC, USA; Department of Biostatistics and Bioinformatics, Duke University, Division of Translational Biomedical Informatics, Durha.
• University of Porto (FEUP), Porto, Portugal; Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Porto, Portugal; Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
• Crit Care Clin. 2024 Oct 1; 40 (4): 827857827-857.

AbstractThis narrative review focuses on the role of clinical prediction models in supporting informed decision-making in critical care, emphasizing their 2 forms: traditional scores and artificial intelligence (AI)-based models. Acknowledging the potential for both types to embed biases, the authors underscore the importance of critical appraisal to increase our trust in models. The authors outline recommendations and critical care examples to manage risk of bias in AI models. The authors advocate for enhanced interdisciplinary training for clinicians, who are encouraged to explore various resources (books, journals, news Web sites, and social media) and events (Datathons) to deepen their understanding of risk of bias.Copyright © 2024 Elsevier Inc. All rights reserved.

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