The Artificial Intelligence (AI) has applications in computational technologies that emulate mechanisms aided by human intelligence. Therefore, a data-based system to predict donations can improve the entire blood supply chain so that more patients receive blood during different situations, reducing the risk of blood unavailability.

The application of Artificial Intelligence aiming at the optimization of processes or making managerial decisions, capable of generating benefits in the most diverse phases. Therefore, so as to meet patient needs and maximize blood lifetime.

1) Protocol: To carry out the systematic review, it is necessary to define a protocol. The literature review was conducted using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyzes) guidelines. The databases used in this study are IEEE Xplore, Science Direct, and Scopus. 2) Research terms: It is important to emphasize that the subject of blood donation can be related to several applications due to its scope and artificial intelligence, which is used in several fields of application, resulting in published studies that are probably not in the proposed content. The search terms, by database, used: “blood donation” AND “artificial intelligence”; “blood donation” AND “machine learning”; “blood donation” AND “deep learning”; “blood donation” AND “machine learning algorithms”; “blood donation” AND “neural network”; and “blood donation” AND “data mining”. After applying all exclusion criteria, 31 studies were selected for further analysis.

Initiatives are found in blood donation phases, from blood donation to blood transfusion. But it is possible to observe that the solutions that involve Blood Donor Selection are very present in the mapped articles, these discuss: blood donor eligibility criteria; fundraising through the location of potential donors; attracting donors in emergencies; donor reaction after donation; a tool for making donor predictions based on your previous contributions; the identification of daily donor patterns; the development of metrics for repeat donors; the factors that influence an individual's decision to donate blood; the classification of donors, checking for seasonal variations; the effect of demographic, cognitive and psychographic characteristics on blood donation; and the motivational initiatives to encourage blood donors to maintain the blood supply. The most used resources are listed in Blood Donor Selection or Storage. According to the selected articles, the focus on Blood Donor Selection is noticeable, as the act of blood donation is not yet a common practice, since, in many places, it is entirely voluntary.

The main problem with blood donation is the lack of blood of a specific type, so can test machine learning algorithms to produce better results. And the problem of donor retention raises concern for blood centers. The choice of method in Blood Donor Selection is the most present. Many techniques related to Artificial Intelligence are used in different studies. Other features with few articles, such as Techniques and methods, Blood Donor Selection, and Laboratory and Transport of blood components, also have relevance in the process, as it helps us to understand other variables that can impact not only the quality of a blood bag for a patient but also in the best use of the effective collection since the act is voluntary.