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PLASTIC RECONSTRUCTIVE AND REGENERATIVE SURGERY

Abstract

From predicting blood demand to enhancing donor recruitment, AI has the potential to improve efficiency and safety. Machine learning helps to manage blood inventory, reduce waste and ensure a timely distribution. Preliminary applications suggest that personalized AI strategies may encourage repeat donations, helping to maintain a stable blood supply.
Emerging screening methods powered by AI have been tested and show promise in detecting potential risks faster and more accurately. AI also has the potential to enhance blood matching, reducing complications and improving patient outcomes. Despite its benefits, AI poses challenges like data privacy, algorithmic bias, and regulatory hurdles. Future research will focus on refining the role of AI, ensuring ethical implementation, and improving transfusion care. With responsible innovation, AI could revolutionize blood transfusion and save lives worldwide.

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Authors

Fabrice Cognasse - INSERM, U 1059 SAINBIOSE, Université Jean Monnet, Mines Saint-Étienne, F-42023, Saint-Etienne, France; Etablissement Français du Sang Auvergne-Rhône-Alpes, Research Department, F-42023, Saint-Etienne, France https://orcid.org/0000-0001-8041-928X

Stéphane Avril - INSERM, U 1059 SAINBIOSE, Université Jean Monnet, Mines Saint-Étienne, F-42023, Saint-Etienne, France https://orcid.org/0000-0002-8604-7736

Julia L. Fleck - Mines Saint-Étienne, Univ Clermont Auvergne, INP Clermont Auvergne, CNRS, UMR 6158 LIMOS, Saint-Étienne, F-42023, France https://orcid.org/0000-0001-7980-1887

Hind Hamzeh-Cognasse - INSERM, U 1059 SAINBIOSE, Université Jean Monnet, Mines Saint-Étienne, F-42023, Saint-Etienne, France https://orcid.org/0000-0003-1462-3893

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