• Yusuff Hakeem Mbbs and Zochios Vasileios Md.
• Department of Anesthesia and Intensive Care Medicine, Glenfield Hospital, University Hospitals of Leicester NHS Trust, Leicester, United Kingdom; Department of Respiratory Sciences, College of Life Sciences, University of Leicester, Leicester, United Kingdom. Electronic address: hakeem.yusuff1@nhs.net.
• J. Cardiothorac. Vasc. Anesth. 2023 Oct 1; 37 (10): 214521472145-2147.

AbstractArtificial intelligence has the potential to improve the care that is given to patients; however, the predictive models created are only as good as the base data used in their design. Perioperative blood management presents a complex clinical conundrum in which significant variability and the unstructured nature of the required data make it difficult to develop precise prediction models. There is a potential need for training clinicians to ensure they can interrogate the system and override when errors occur. Current systems created to predict perioperative blood transfusion are not generalizable across clinical settings, and there is a considerable cost implication required to research and develop artificial intelligence systems that would disadvantage resource-poor health systems. In addition, a lack of strong regulation currently means it is difficult to prevent bias.Crown Copyright © 2023. Published by Elsevier Inc. All rights reserved.

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