Blood donation is an essential pillar for the sustainability of health systems, as it guarantees the availability of essential blood components for surgical procedures, emergencies, oncological treatments, among others. However, donor selection continues to be a major challenge, as it requires a balance between transfusion safety and sufficiency of supply. Traditionally, this process has been based on structured interviews applied by health professionals, but these can be affected by subjective factors, such as the interviewer’s experience, workload or interpretation of regulatory criteria.

In this context, AI emerges as an innovative tool that promises to transform transfusion medicine. Its ability to process large volumes of data, identify patterns invisible to the human eye and offer standardized decisions makes AI a strategic ally in donor selection and management. Likewise, donor selection is a critical process in transfusion safety, as the incorporation of AI would provide opportunities to optimize eligibility assessment, improve donor return prediction and optimize inventories.

The (IA) in eligibility assessment

One of the main contributions of AI is standardization in the application of selection criteria. While an interviewer may interpret differently certain medical history or lifestyle habits, an AI-based system can process this information in a uniform way, reducing variability and the risk of error (Singh et al. 2024) point out that the integration of AI into screening questionnaires can provide immediate and evidence-based answers at a higher level. This orientation does not seek to replace the health professional, but rather to reinforce his or her clinical judgment, freeing him or her from repetitive tasks, focusing on humanized accompaniment, reaffirming empathy towards the donor and thus allowing him or her to guide the donor in the safety of the donation process.

In addition, the expected operational impact, within the supply chain in blood banks and hospital services, establishes an association towards the combination of forecasts for each blood component (Red Blood Cell, Plasma, Platelets and Cryoprecipitates) with surgical schedules, hospital needs, among others, thus allowing to adjust collections, inter-hospital transfers and stock levels, which several studies and reviews associate with reductions in stock-outs and expirations, thus meeting the demand for blood product management. (Barzilai, 2025; Cardona et al, 2025).

The selection process requires the application of extensive, changing and specific criteria according to national and international regulations. Recent studies have shown that AI systems allow to generalize decisions and reduce errors in the application of eligibility questionnaires (Global Journal of Transfusion Medicine, 2024). Unlike human intervention, AI is not affected by emotional or cognitive factors, ensuring consistency in the initial assessment.

Return prediction, donor loyalty and supply management.

Donor loyalty is another critical challenge. Despite campaigns, many donors do not return, which directly affects the capacity of the blood component inventory. Here, AI has demonstrated significant value. Machine learning models such as XGBoost and SVM achieved significant and relevant data as a support tool, in predicting donor return, which allows segmenting and targeting personalized communication strategies (Shah et al., 2022). With this information, blood banks can design more effective reminders, campaigns tailored to the donor profile and recognition programs that encourage recurrence.

In the search for innovative strategies to address blood shortages, especially of rare or infrequent blood groups, blood banks are exploring various tools and techniques. Among them are gamification models that have proven effective in increasing recruitment of potential donors by spreading awareness and encouraging people to donate. These models, which teach through gamification, feature three essential moments: the narrative (game story), the mechanics (badges, points, medals) and the reward (physical or virtual). (Rakesh, Sharma., et al. (2022).

Blood supply planning relies heavily on the ability to predict donor return. Learning-based models have been shown to identify with high accuracy the factors that predict the likelihood of an individual returning to donate, even in critical scenarios such as the COVID-19 pandemic (British Blood Transfusion Society, 2024). These advances make it possible to design more effective loyalty and personalized communication strategies.

(Velásquez, A. 2024) Reports how the Banc de Sang i Teixits (BST) in Catalonia in 2020, implemented the BST-Analytics program that managed to optimize donor selection between two and four times, using previous years’ data processed by an algorithm. It identified donation patterns, analyzed donor behavior and evaluated the effectiveness of past campaigns. It was also able to determine the most productive areas for collection, collection strategies and discovered the reasons why donors interrupted their donations for years.

Waste reduction and resource optimization

The institutional management of blood components faces a constant problem: wastage, specifically in the platelet component, whose useful life is limited. (Zhou et al. 2024) report that the use of predictive models based on AI allowed a reduction in wastage of almost 14% with respect to the conventional strategy that has been applied. These results demonstrate how AI not only improves donor selection, but also has a direct impact on the efficiency and sustainability of Pretransfusion Management Services.

In blood banks, AI helps to predict demand and optimize the use of resources. Through pattern recognition algorithms, it is possible to prioritize units, identify non-compliant products and reduce losses associated with the expiration of blood components (Global Journal of Transfusion Medicine, 2024).

Smart donor recruitment

Digital platforms and social networks represent a new scenario for interaction with potential donors. A relevant example is the tool implemented by Facebook, which, through matching algorithms between donors and local donation opportunities, managed to increase by up to 10% the probability of scheduling appointments to donate (Goldstein et al., 2021). This type of innovation shows that AI can diffuse the screening phase and also become a proactive onboarding engine.

Entities such as The American Red Cross (Rakesh, Sharma., et al. (2022), by incorporating gamification models, have been able to teach about compatibility through gamification, which resulted in increased recruitment of potential blood donors; other apps such as ‘Blood Hero’ combines gamification and social media to attract users’ attention, as it allows potential donors to connect and inspire their contacts through social media.

A vision of the future with ethical challenges

Yes, while the positive impact of AI is indisputable, there are challenges that should not be ignored: privacy, data security, algorithmic bias and acceptance by healthcare personnel. The challenge is to find a balance between technological innovation and respect for the ethical and regulatory principles governing transfusion safety.

Implementation considerations

Despite its benefits, AI in donor selection plans relevant challenges. The ambiguity of complex models such as deep neural networks limits their clinical interpretability, raising concerns about confidence in automated decisions (Yu, 2024). Furthermore, if training data are unrepresentative, there is risk of biases affecting fairness in donor selection (Tjoa & Guan, 2019). Finally, clear regulatory interventions are required to ensure privacy protection and ethical oversight of the process (Yu, 2024).

Artificial intelligence (AI) is emerging as a strategic ally in the selection and management of blood donors. Its contributions in the standardization of criteria, return prediction, waste reduction and digital recruitment represent concrete advances towards a safer, more efficient and sustainable transfusion system. However, the success of its implementation will depend on a responsible approach that combines the accuracy of the algorithms with human sensitivity and the regulatory scopes established for its control and monitoring. Blood donation, beyond a technical process, is an altruistic act that involves trust, solidarity and social commitment. AI is already demonstrating a substantial impact on multiple stages of blood donation: from predicting donor behavior, optimizing campaigns and logistics, to constituting future demand and reducing waste.

Therefore, the incorporation of AI in donor selection has the potential to strengthen transfusion safety through more objective evaluations, behavioral prediction, logistic optimization and better clinical outcomes. Thus, its implementation should be accompanied by strategies that ensure transparency, equity, normative regulation and monitoring of health personnel. The responsible integration of these technologies can represent a decisive advance for transfusion medicine and safety and the efficient management of blood components.

References

-British Blood Transfusion Society. (2024). The use of predictive modelling to determine the likelihood of donor return during the COVID-19 pandemic. Transfusion Medicine.

-Barzilai, M. (2025). AI applications in transfusion medicine. Acta Haematologica, 153(1), 1-14.

-Cardona, D. C. V., Poveda, C. D., & Botero, J. C. (2025). Artificial intelligence techniques in blood banks. Acta Haematologica Polonica, 56(2), 1-10.

-Da Silva, G., et al. (2021). Use of Artificial Intelligence in blood donation: A systematic review. Hematology, Transfusion and Cell Therapy, 43(5).

-Global Journal of Transfusion Medicine. (2024). Artificial intelligence in transfusion medicine – subspecialties. Lippincott Williams & Wilkins.

-Goldstein, D., et al. (2021). Matching blood donors with opportunities: evidence from a large-scale field experiment.

-Rakesh, Sharma, et al. (2022). Smart approaches for encouraging the blood donation. Asian Journal, Sep 28;18(2):303-315.

-Singh, H., et al. (2024). Integrating Artificial Intelligence into the Donor Selection Process: Opportunities and Challenges. Global Journal of Transfusion Medicine, 9(1), 1-5.

-Shah, S., et al. (2022). Prediction of blood donor return using machine learning methods. PLoS ONE, 17(11).

-Tjoa, E., & Guan, C. (2019). A survey on explainable artificial intelligence (XAI): Towards medical XAI. arXiv.

-Velásquez, A. (2024). Artificial Intelligence drives blood donation and develops key medical innovations. Expo med Hospitalar Mexico Media.

-Yu, S. (2024). The ethics of using artificial intelligence in medical research. Kosin Medical Journal.

-Zhou, Y., et al. (2024). Reducing platelet wastage using machine learning-based predictive models.

 

“In a world where every blood donation can mean a life saved, artificial intelligence emerges as an unexpected ally capable of anticipating, optimizing and transforming the way we care for our most vital resource.”