Artificial intelligence in hemovigilance: A narrative review on advancing blood safety and monitoring systems

Abstract

Background

Blood transfusion is essential for patient safety, yet traditional hemovigilance systems face challenges including underreporting, data integration issues, and slow response times. Artificial intelligence (AI) offers solutions through advanced information systems capable of identifying, analyzing, and reporting transfusion events.

Objective

This narrative review examines the potential of emerging AI technologies to enhance hemovigilance, focusing on data integration, adverse event detection, personalized risk management, and blood supply chain optimization.

Methods

A comprehensive literature review was conducted using PubMed, Scopus, IEEE Xplore, Web of Science, Google Scholar, and Embase, covering studies from 2010 to 2024. AI applications, including machine learning, deep learning, natural language processing (NLP), and predictive analytics, were analyzed for their impact on transfusion safety and operational efficiency.

Findings

AI improves real-time data acquisition, detection of transfusion-related adverse events, predictive risk assessment, and supply chain management through demand forecasting and waste reduction. NLP facilitates integration of unstructured clinical data, while AI-driven decision support systems enable proactive and personalized patient care.

Limitations

Challenges include data privacy, algorithmic bias, regulatory gaps, and dependency on data quality. Future directions involve federated learning, explainable AI, and standardization to ensure secure, transparent, and equitable AI-based hemovigilance.

Conclusion

AI has the potential to revolutionize hemovigilance, improving patient safety and efficiency, while addressing operational and ethical challenges.

Introduction

Hemovigilance is a system composed of several components and has been developed to secure the transfusion process from the beginning to the end, which includes monitoring adverse reactions of patients to blood transfusion initially. ¹ It is an important element of transfusion medicine, which provides information on the areas of safety that are lacking and the potential for interventions aimed at improving patient outcomes. ² Despite this, classical hemovigilance systems are confronted with serious issues such as the underreporting of adverse events, the disparity in the definitions of data, and the grading systems which make it difficult to compare the data. ¹ Manual reporting and the slow response times are obstacles for the effectiveness of the systems. The measures taken to address these issues, such as the implementation of transfusion practitioners and the promotion of teamwork in the hospital transfusion committees, have yielded some positive results. ³ The introduction of artificial intelligence (AI) in the healthcare sector is now thus changing the way transfusion medicine is practiced. The AI technologies, consisting of machine learning (ML) and deep learning algorithms, offer creative tools for automatic data analysis and increased diagnostic accuracy. ⁴ In laboratory medicine, AI applications cover the areas of operational decision-making, automation of workflows, error detection, prediction, and interpretation of results. ⁵ The potential of these advances to revolutionize hemovigilance is enormous, by increasing the efficiency of identifying adverse events, optimizing treatment plans, and in the long run making transfusion medicine safer for patients. ³

Objective and scope of the review

This review aims to explore the integration of AI in hemovigilance systems, showing its potential to relocate to the field of blood transfusion safety rather than its detection of adverse events and optimization of the blood supply chain. The study includes exploring AI-powered innovations like ML, natural language processing (NLP), and predictive analytics, besides touching upon issues like data privacy, algorithmic biases, and regulatory frameworks. The review dives into the AI's ability to revolutionize transfusion medicine and the so-called safer, more efficient healthcare practices. Solutions based on AI and ML are being brought to supplement detection and application of destructive occasions related to blood transfusions with more advantageous chance evaluation and predictive analysis. This technology can examine large quantities of information, which includes patient records, transfusion history, and adverse event reviews, and identify patterns and risk factors not previously available with conventional techniques. This type of facility can assist in identifying high-risk patients early in their care and initiating prevention techniques, so that you can bring about advanced effects in patients. One of the critical packages of AI in hemovigilance is in predictive analytics for risk control. With the assistance of past facts, AI can also predict capacity complications like febrile nonhemolytic transfusion reactions or hypersensitive reactions, enabling clinicians to undertake more proactive treatment. AI is likewise capable of optimizing the blood supply chain. With the call for making plans and stock planning, AI can prevent wastage and maximize the efficiency of blood series and distribution. Methods like lateral transshipment among hospitals and stock sharing also enhance the performance of the supply chain. Utilization of AI, along with upcoming technologies like blockchain, also can enhance transparency and protection within the blood deliver chain. Some of the ML algorithms hired to identify styles and anomalies in transfusion records and in clinical internet of things (IoT) gadgets are random forests, support vector machines (SVMs), and neural networks. These algorithms have sure strengths: random forest is robust and green with high-dimensional facts, SVM is effective for class and outlie. Finally, we explore future directions for AI in transfusion medicine, emphasizing the need for international collaboration, standardized protocols, and ethical AI deployment to ensure equitable advancements in global blood safety. This comprehensive analysis aims to provide actionable insights for policymakers, healthcare providers, and researchers working toward safer, more efficient transfusion practices worldwide.

Methodology

This narrative review was conducted in order to explore the impact of AI on hemovigilance referencing its function in the fields of safety, efficiency, and accuracy of blood transfusion monitoring and adverse event detection. The review combines the recent progress in AI technologies like ML, NLP, and predictive analytics and their application in strategies for improving hemovigilance systems. The research was based on the extensive analysis of the related studies, case studies, and the AI-driven innovations that have been conceived to estimate the benefits and drawbacks of introducing AI into the practices of transfusion medicine.

Literature search strategy

This review includes studies from multiple reputable sources such as PubMed, Scopus, IEEE Xplore, Web of Science, Google Scholar, and Embase. The following keywords and phrases were used in various combinations to conduct the search:

“Artificial intelligence in hemovigilance”

“AI in blood transfusion safety”

“Hemovigilance and machine learning”

“AI applications in healthcare safety”

“Predictive analytics in hemovigilance”

“Artificial intelligence in adverse event detection”

“IoT and hemovigilance”

“AI algorithms for blood safety”

“Data analytics in hemovigilance”

“Machine learning in transfusion medicine”

These search terms were selected to identify relevant studies on the integration of AI in improving blood transfusion safety, adverse event reporting, and operational efficiency in hemovigilance systems.

Timeframe and selection criteria

The chosen studies were the ones that are relevant to the review's focus on AI and its role in hemovigilance. At the very first stage, they were meaningful to the designated titles and abstracts; therefore, they were subjected to inclusion and exclusion criteria. Full texts of eligible articles were then inspected for further evaluation. The Pollock et al.'s systematic review was relevant to the research, so papers published from 2010 to 2024 were used to come up with the state-of-the-art studies concerning AI applications. The selection covered peer-reviewed articles, and research, and reviews, as well as case studies, the latter of them focused on AI applications in the field of hemovigilance and adverse event detection and blood transfusion safety. However, sources that are not scientific peer-reviewed, such as gray literature or conference abstracts, as well as studies not AI-focused or have nothing to do with hemovigilance or nonhealthcare applications were not included.

Data extraction

Relevant data from the selected studies were extracted, including:

Study characteristics: Authors, publication year, study design (e.g. case study, review, experimental study), and geographical region.

AI applications: Description of the specific AI technologies used (e.g. ML algorithms, deep learning, NLP) in hemovigilance, including their role in improving blood transfusion safety and adverse event reporting.

Outcomes: Key findings regarding AI's role in improving the detection of adverse events, automation of reporting processes, predictive analytics, and enhancing the accuracy of blood transfusion safety monitoring (Figure 1).

Figure 1.

PRISMA flowchart for AI in hemovigilance review. AI: Artificial intelligence.

Critical analysis and interpretation

1. Evaluation of methodological quality: A structured and comprehensive evaluation framework was applied to ensure methodological integrity across all included studies. Each paper was critically reviewed based on research design, transparency of AI methodology, validation strategy, and clinical relevance to hemovigilance. Priority was given to studies employing robust AI models such as random forests, SVMs, and deep neural networks supported by clear data processing methods and validation metrics, including sensitivity, specificity, and predictive accuracy. Studies demonstrating a direct link between AI-driven tools and measurable improvements in transfusion safety, adverse event detection, and operational efficiency were considered as contributing high-value evidence. This structured approach allowed for a balanced assessment of both technological advancement and clinical applicability, establishing a strong foundation for evidence-based interpretation.

2. Contextualization and thematic synthesis: The analysis adopted a thematic synthesis approach to derive meaningful insights from the literature and to identify prevailing trends in AI applications for hemovigilance. After systematic review and coding of the selected studies, five overarching thematic domains emerged:

3. Adverse event detection and reporting systems: Enhancing precision and responsiveness through predictive modeling and automated surveillance mechanisms.

4. Predictive analytics and risk stratification: Leveraging AI to forecast transfusion reactions and guide personalized preventive strategies.

5. Optimization of blood supply chain and resource management: Utilizing intelligent algorithms for demand forecasting, inventory control, and logistic efficiency.

6. Ethical and regulatory synergy: Advancing transparent, secure, and ethically governed AI frameworks aligned with global standards.

7. Each thematic category was analyzed in relation to its contribution to patient safety, efficiency, and innovation within transfusion medicine. The synthesis revealed a cohesive evolution in hemovigilance—from manual reporting toward a dynamic, data-driven, and intelligent monitoring ecosystem. This interpretation underscores AI's transformative capacity to enhance decision-making, predictive accuracy, and operational excellence across all dimensions of blood transfusion safety.

Identifying gaps: Gaps in the literature will be highlighted, including the need for more robust clinical trials, larger datasets, and longitudinal studies to validate AI-driven approaches in hemovigilance.

Results

Transforming hemovigilance with AI innovations

The integration of AI into hemovigilance systems has significantly improved blood transfusion safety and operational efficiency. AI technologies enhance various aspects of hemovigilance, including real-time data acquisition, adverse event detection, predictive analytics for transfusion needs, and blood supply chain management optimization, despite ongoing challenges related to data standardization and system interoperability.

Seamless data collection and integration in hemovigilance

AI has become a revolutionary instrument in hemovigilance, especially in overcoming the problems of collecting and integrating data. The key aspect of this progress is the employment of NLP techniques, which make it possible to handle unstructured data from electronic health records (EHRs) and other sources. Unstructured clinical narratives in EHRs provide extensive and patient-specific data that are necessary for hemovigilance. NLP-enabled AI systems can pull this data out and examine it rapidly, thus the discovery of transfusion-related incidents and other main hemovigilance indicators becomes possible. ⁶ AI plays a pivotal role in hemovigilance, not only facilitating data collection but also linking and harmonizing this data across different sources, through the introduction of NLP, which is necessary for the analysis of unstructured data (Figure 2). For example, NLP techniques have been successfully utilized to detect adverse drug reactions from pediatric EHRs through the pairing of drugs–symptoms with regulatory repository resources like the food and drug administration (FDA)'s Adverse Event Reporting System. ⁷ Pointing out the methods that result in a breakthrough of AI in terms of data extraction and integration through the use of techniques. The evolution of high-tech, clinical language models has also completely changed hemovigilance data processing. Models like GatorTron, which have been trained by de-identified generated text sourced from over 82 billion words, have achieved near-perfect performance for a wide array of clinical NLP tasks including concept extraction and medical question answering to a high degree. These strengths enable AI models to extract detailed insights, identify relevant information, and generate meaningful data from large datasets, thereby enhancing the effectiveness and efficiency of hemovigilance systems. ⁸

Figure 2.

Role of AI in hemovigilance. ⁴⁵ This figure illustrates the integration of AI tools in the monitoring and reporting of adverse events in blood transfusion practices, highlighting the potential for predictive analytics and automated data processing in improving patient safety. AI: Artificial intelligence.

Enhanced detection and reporting of blood products induced adverse events through AI

AI and ML have served as tremendous means for the identification and notification of adverse events related to blood transfusions as well as for the general hemovigilance. ML models are utilized to predict outcomes, assess risks, and detect complications such as hemorrhage in trauma patients that require transfusions. These models have exhibited notable qualities over traditional standards by enhancing predictions of mortality and the development of patient-specific scoring systems. AI-driven ML technologies are moving progressively to become a tool in trauma management that aids doctors to come up with a patient-specific diagnosis. ⁹ Existing national hemovigilance systems, such as the National Healthcare Safety Network (NHSN) Hemovigilance Module in the United States, play an important role in monitoring transfusion-related adverse events. However, although these systems are not AI-based, their comprehensive databases provide the data that can be used for ML analysis, which will increase the accuracy and efficiency of adverse event detection. Also, resources like the notify library import hemovigilance data to be used for both educational and reference purposes, therefore very supportive of safety in transfusion practices. ¹⁰ Despite the advancements, the following disadvantages exist: the majority of the ML research on hemovigilance and trauma care is retrospective in nature, with little validation in real-world clinical settings, either prospective or ongoing. Even though prediction models are available for transfusions requirements and coagulopathy, they are not widely used in clinical practice which is an indication of the theory–practice gap.^9,10

Leveraging predictive analytics for risk management in hemovigilance

AI has the capability to be a game changer in hemovigilance programs through predictive analytics and personalized risk assessments, which in turn, improve safety of the patient and transfusion practices. Through processing of vast arrays of data consisting of patients’ background information, transfusion histories, and adverse event reports, AI-based predictive algorithms can map out intricate patterns and risk factors connected to transfusion reactions. These revelations serve as stepping stones toward the early identification of high-risk individuals, thereby equipping clinicians with the power to put in place-specific preventive measures. AI models, for example, can predict the likelihood of the most common reactions such as the febrile nonhemolytic transfusion reactions or allergic responses, therefore enhancing the application of proactive care strategies.^11–13 AI-based decision support systems have the potential to integrate patient-specific data with clinical guidelines and risk stratification tools, which, in turn, allow for the individualization of transfusion plans. The systems suggest data-driven best practices for the choice of blood components, the assessment of the need for medication before the transfusion, and the development of monitoring protocols suitable for personalization. The risk of serious complications, such as transfusion-associated circulatory overload and transfusion-related acute lung injury, which are potentially fatal, is diminished as this level of personalization is implemented. ¹⁴ Besides these individual strategies result in increased safety of the transfusion process, they also provide the method to the efficient use of resources and the reduction of healthcare costs. ¹²

Optimizing blood supply chain efficiency with AI integration

AI has become a groundbreaking technology in blood supply chain management, with better demand forecasting, reduction in waste, and safety and traceability becoming easier as results. AI-based decision support systems that use ML and time series forecasting models can be the best ones in all the blood supply chain processes. This might mean things like how much you will need, how you deal with a donor, or the fixed blood donor schedules that together bring to the table heightened blood collection, reduced inventory wastage, and eliminated shortages. ¹⁵ The sophisticated inventory models and AI algorithms contribute to better blood inventory management, however, management practices are more important compared to the most cutting-edge tech, according to the latest research. For example, incorporating ploys of commercial supply chains like stock sharing or lateral transshipment due to expiring blood units between hospitals can incredibly boost the entire supply chain as a whole. ¹⁶ The mashup of AI together with upcoming technologies like blockchain only further ignites the possibility of blood supply chain management. Blockchain provides means of increasing transparency, traceability, and security, thereby, mitigating the potential risks like errors or frauds that may occur during the donation-to-transfusion process. ¹⁴ Globally, during the COVID-19 pandemic, AI-based multivariate time-series deep learning models have proven to be effective enough to predict the demand for donations and maintain a satisfactory inventory level, hence allowing the continuous delivery of patients care.^17,18

AI technologies transforming hemovigilance practices

AI increases efficiency in hemovigilance by using ML algorithms such as SVMs, random forests, and neural networks which are extensively used in the health sector for pattern and anomaly detection in huge datasets from transfusion records or medical IoT devices.^19,20 Random forest is an algorithm, which is noted for its resilience and great performance in different areas of application and is also an algorithm which is used in the healthcare domain. It is very effective when dealing with datasets that have a high number of features and is less likely to overfit compared to other algorithms. ²¹ SVMs are especially useful for classifying objects in the medical record and for anomaly detection and can deliver solutions with less training data. ¹⁸ Furthermore, neural networks with their deep learning capabilities are well-suited for the discovery of concrete patterns in the very large dataset, and as a result of such patterns they are the key technology for hemovigilance. ²² For the processing of the large numbers of the data that is generated by the medical IoT devices and the transfusion records, big data analytics frameworks such as Apache Spark are the only viable solutions, and they enable the processing of the datasets to be distributed. The addition of techniques that involve dimensionality reduction and feature extraction will give you more tools for proper handling of high-dimensional medical data. ²³ Moreover, IoT devices with edge-computing capability are able to conduct real-time monitoring during transfusions and thus anomalous events will be immediately detected and assist in the increase of patient's safety. The employment of these AI-driven techniques in combination ensures that the hemovigilance systems can be highly effective in monitoring and improving transfusions. ²⁴

Real-world applications and case studies of AI in hemovigilance

The incorporation of AI in hemovigilance has emerged as a feasible strategy to enhance healthcare outcomes and the credibility of adverse event reporting in the systems. An analysis detected 125 adverse event signals among 26 System Organ Classes, comprising 108 previously unknown signals, thereby confirming the potential of AI to detect security issues that do not appear to be clear but are hidden in real-world data. ²⁵ Moreover, one of the most ingenious uses of AI is demonstrated through DeepSAVE, which is a deep learning framework designed to precisely identify user query logs that contain adverse events. It presented a solution to the problem of comprehension in the diverse search settings and the variety of user behavior levels. In contrast to the available methods, DeepSAVE led in achieving the best results while it was used in three vast authentic datasets, thus supporting the fact that AI has the power to utilize the online search data for quick learning of potential adverse events. ²⁶ The AI safety monitoring assistance in educational situations has been put under investigation. A research project that involved health science students who participated in AI-enhanced simulations discovered that the intervention group reported more medication-related adverse events than the control group. The results of this study suggest that AI-based simulations can enhance the participants’ ability to think systemically and accurately report, thus resulting in the practice of the safety monitoring and reporting processes. ²⁷ In spite of the promises brought by the development, some issues still remain to be addressed regarding the use of AI for adverse event detection. ²⁸

Discussion

AI technologies in hemovigilance are now ensuring enhanced safety, efficiency, and accuracy from blood transfusion processes through the implementation of the new operations. AI-enabled predictive analytics and decision support systems have been shown to mainly focus on their features to foresee the possible safety problems in advance and raise the patient's outcome accordingly. Using patient records collected over a wide area of past cases, these systems can uncover unseen patterns and risk factors, and thereby lead to proactive interventions and the development of personalized transfusion strategies. ²⁹ Besides, the AI algorithms applied to medical imaging have improved their diagnostic precision by allowing rapid and accurate evaluation of blood transfusion-related complications. On the one hand, AI offers its promise; on the other hand, traditional hemovigilance practices persist. Research studies have proved the transfusion reaction rates that range from 0.27% up to 0.3%, thereby emphasizing the necessity for constant and vigilant supervision.^30,31 The most frequent reactions reported were allergy, febrile nonhemolytic transfusion, and acute hemolytic reactions. ³² The hemovigilance transfusion chain systematically monitors side effects and protects activities that contribute to safety and quality. ³³ This highlights the importance of integrating advanced AI capabilities with traditional methods to ensure comprehensive monitoring and enhanced patient safety.^30,31,34 The inclusion of AI in hemovigilance systems prompts many benefits like the prompt determination of events, the decision-making processes through evidence-based knowledge, and the simplification of monitoring. The issues like data protection issues, algorithmic biases, and the absence of strong regulatory frameworks need to be overcome in order to take advantage of AI fully. As AI technologies improve and express partnership with traditional hemovigilance methods, the safety blood monitoring system will be more robust and effective, hence, better transfusion practices and healthcare outcomes could be introduced. ³⁵

The integration of AI and explainable AI in healthcare is faced with many issues that need to be improved to make it effective. One of the key points is to ensure transparency and the ability to integrate with the existing healthcare infrastructure. In the future, AI might become more widely used in crucial areas such as healthcare if it will be able to provide accurate and comprehensible explanations of its decisions and predictions. Nevertheless, the complexity of deep learning models often leads to “black-box” systems, which underscores the requirement for developing AI models that are more interpretable and transparent. ³⁶ This aspect is particularly important in the medical domain because AI-generated errors may lead to disastrous results. The privacy of patients and the security of their data are other significant issues. The implementation of AI requires the use of strong measures to protect healthcare data from breaching at the same time being in compliance with regulatory and ethical standards. ³⁷ Adequate consent management and adherence to cybersecurity protocols play a vital role in promoting patient autonomy and maintaining the integrity of data. To solve these problems, first and foremost we need clear regulatory frameworks and transparent validation processes of AI models. Besides, the creative process of cybersecurity, and ethical policies should be worked out to introduce the AI systems in a proper way. Moreover, universal standards for interoperability and equal access to AI technologies are mandatory to avoid inequalities in medical treatment. ³⁸ The cooperation between healthcare professionals, policymakers, and researchers is required, thus it will stimulate interdisciplinary innovations and help AI technologies become a part of the clinical practice. ³⁹

Emerging trends and future prospects of AI in hemovigilance

The future of AI in hemovigilance and healthcare relies on the creation of standards that incorporate the integration of AI with other systems and exercising of privacy-protective techniques such as federated learning. ³⁸ Federated learning is a new way that enables data privacy while AI models are improved. This technique enables the training of the neural networks in remote, distributed edge devices collaboratively by avoiding the case to pass the raw data. Consequently, patient detail confidentiality is maintained, and privacy regulations such as the General Data Protection Regulation and the Health Insurance Portability and Accountability Act are upheld. ⁴⁰ It aids in the establishment of a healthcare open ecosystem by enabling knowledge sharing to be done from widespread and varied datasets but with privacy being maintained. ⁴¹ However, federated learning has both advantages and disadvantages, with the drawbacks being the distortion of the model due to the channel fading and poor aggregation of locally trained models on the unbalanced data. ⁴⁰ Further development in the future might be achieved by combining digital twin technology with federated learning, which could shape smart city healthcare network applications. ⁴² Besides federated learning, global AI standards in hemovigilance and healthcare should be implemented. Consistency, interoperability, and fairness will be the main guarantees of these standards, while at the same time they will guarantee the security of trust and the acceptance of them clinically. The incorporation of AI in different datasets and automating of workflows would even make AI more valuable in hemovigilance programs. Hence, AI's growing role evolves, the problems of generalizing algorithms, validating the clinical procedures as well as integrating advanced technologies such as large language models will be of utmost importance in the healthcare delivery transformation. ⁴³

Limitations

A key challenge is the variability in data quality. Hemovigilance data, often collected through multiple sources, can be inconsistent, incomplete, or poorly standardized, which hinders AI systems from making accurate predictions. Inaccurate or missing information about patient history, transfusion details, or reaction severity may affect AI's ability to detect trends or assess risks properly. Another limitation is the AI's reliance on existing data patterns. If a reaction is rare or atypical, AI might struggle to identify it, as it depends on historical data to recognize patterns. This can delay the detection of novel adverse events that were not previously encountered or documented in databases.

A comprehensive review of the state-of-the-art automatic trigger tool-based methods that were used for adverse event detection in EHRs showed a sharp divergence in the prevalence of adverse events and positive predictive values across the studies. ⁴⁴ The discrepancies were due to the differently described methodologies, which caused the interpretation difficulties. This discovery pointed to the fact that there is a need to have unified procedures and reporting guidelines in place to ensure uniformity and trust in AI systems.

Additionally, AI systems may not fully account for the complexity of individual patients. Variations in underlying health conditions, comorbidities, or genetic factors may influence how a patient reacts to a transfusion. AI models often lack the depth of human clinical expertise needed to assess these nuanced variables. Finally, AI in hemovigilance requires continuous updates and training to adapt to emerging knowledge. Without regular input from medical professionals and the latest research, AI systems risk becoming outdated or unable to respond to new challenges in transfusion safety.

Conclusion

AI has become the most significant force in health sector transformation, bringing fresh strategies to clinical decision support, diagnostics, and patient management. Implementing it in hemovigilance systems is likely to be a real opportunity for safety, monitoring, and reporting of transfusion-connected adverse events to be successfully improved and operated. Sitting on AI's shoulders, in data analysis and pattern recognition, hemovigilance programs can outpace the existing ones in detecting adverse effects, risk evaluation, and patient safety. Nevertheless, the implementation of AI in hemovigilance relies on addressing the crucial problems of data availability, digital infrastructure, ethics, and regulatory compliance, especially about the less developed countries. Passing the difficulties of these barriers will call for the joint action of clinicians, data scientists, policymakers, and other concerned parties to see AI responsibly and effectively utilized in healthcare systems. Moreover, the latest developments in AI techniques such as explainable AI and privacy-preserving technologies like federated learning, constitute the way to bring a well-transparent and secure AI-based hemovigilance framework. As technologies develop, they have to comply with international standards and guarantee the inclusion of all in different healthcare settings. To sum up, while AI delivers countless possibilities to increase hemovigilance, its current potential will be fully realized only in the case of a proper balance between technical, ethical, and operational problems and the progress achieved in these areas. Through encouraging interdisciplinary collaboration and innovation, AI can play a very crucial role in the development of transfusion medicine, which ultimately ensures patient safety and healthcare outcomes.

Purpose of the review

This review aims to explore the potential and challenges of integrating AI into hemovigilance systems to enhance blood safety and monitoring. It aims to highlight how AI can improve the detection, reporting, and analysis of adverse reactions during and after blood transfusions, thereby enhancing patient outcomes. This review highlights AI's capacity to process large datasets, identify patterns, and predict risks more accurately and efficiently than traditional methods. However, the methodology does not explicitly detail how these capabilities were assessed. Additionally, it addresses the limitations and hurdles in implementing AI, such as data quality issues, the need for standardization, and its ability to detect rare or novel reactions. By critically analyzing the current state of AI in hemovigilance, this review provides insights into its potential for revolutionizing blood transfusion safety and discusses areas for future research and development. The goal is to inform healthcare professionals, researchers, and policymakers about the promising role AI could play in improving transfusion practices and patient care.