How AI Could Help Solve the Global Blood Shortage Crisis

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“Blood deserts” can benefit from high-tech solutions

by

Shreenik Kundu, MBBS, and Robert Glatter, MD

January 7, 2src25

In many parts of the world, life-saving blood transfusions remain a luxury. While urban areas in countries like the U.S. and the U.K. have long-established systems for blood collection and distribution, millions in low- and middle-income countries (LMICs) face a far grimmer reality. In regions with inadequate healthcare infrastructure, scarcity of blood can be deadly in areas referred to as “blood deserts.” Every year, millions die from the lack of a simple yet essential medical resource: blood.

But what if there was a way to predict when and where blood will be needed most? What if we could track shortages in real time, mobilize donations instantly, and ensure blood banks are never empty? As we stand on the edge of a digital health revolution, many experts believe that the answer to these questions lies in artificial intelligence (AI).

The Current Crisis: Blood Deserts

In LMICs, healthcare systems often struggle to meet even basic needs, and blood is no exception. According to the World Health Organization, high-income countries collect a median of 31.5 units of blood per 1,srcsrcsrc people annually, while low-income countries manage just 5 units per 1,srcsrcsrc people. The gaps are widest in rural and remote regions, where hospitals may lack both donors and the ability to store blood properly.

For example, in India, vast regions experience blood deserts, where blood is almost impossible to obtain in emergencies. A similar situation exists in parts of sub-Saharan Africa, Southeast Asia, and North and Latin America. Without readily available blood, patients suffering from trauma, childbirth complications, or medical conditions like anemia or cancer face higher mortality rates.

Why AI is the Answer

AI, when integrated into healthcare systems, offers the potential to revolutionize blood supply management. By analyzing massive datasets and applying machine learning and deep learning algorithms, AI can predict blood demand with surprising accuracy. This means that instead of reacting to shortages after they occur, healthcare providers and administrators could anticipate and even predict them and take preventive action.

India’s e-RaktKosh system is already demonstrating how real-time data can transform blood availability. Launched by the government to provide an open-source platform that tracks blood donations and stock across the country, e-RaktKosh enables hospitals and blood banks to monitor local supply levels in real time. This transparency has proven useful in coordinating the timely distribution of blood, particularly in rural areas. The system has registered over 6.4 million donors and collected more than 6.6 million units of blood since 2src22. However, while the e-RaktKosh system is a major step forward, it is not yet optimized for predictive modeling or demand forecasting. This is where AI could make a difference.

Applying AI to Blood Availability

At its core, AI excels at identifying patterns in large datasets and making predictions based on those patterns. In the case of blood availability, an AI-enabled system could analyze data on blood donation rates, hospital admission trends, population health statistics, and even traffic conditions to predict when and where blood will be needed most.

For example, in a country like Kenya, where healthcare data is often not digitized, AI could provide the technological infrastructure to make sense of fragmented information. By integrating AI with geospatial mapping, healthcare professionals could identify blood deserts and deploy mobile blood collection units or other solutions like drone deliveries to the areas where they’re needed most.

In Mexico, where retrospective data is being used to estimate demand for blood, AI could offer more accurate, real-time models that account for ongoing events like public health emergencies or natural disasters. This would help authorities respond dynamically to changing needs.

Challenges to Overcome

Of course, the integration of AI into blood availability systems is not without its challenges. The most significant hurdle remains data availability. Many LMICs struggle with incomplete or poorly digitized health records. Without reliable, real-time data, even the most advanced AI systems would be left guessing.

This is why experts argue that the focus should not solely be on AI but on improving data collection and infrastructure. Governments must prioritize the digitization of healthcare systems and ensure that data is accessible, accurate, and secure. Only then can AI fulfill its potential.

Moreover, while AI has advanced rapidly in developed countries, rolling out AI-based systems in resource-constrained settings will require international cooperation and investment. Governments, non-profits, and private tech companies will need to work together to build capacity and train local healthcare workers to use these technologies effectively.

There is also the obvious question of actually getting the blood where it’s needed. Deployment of drone delivery systems and infrastructure to coordinate rapid identification of blood donors for ground transportation to donation centers are part of the process that AI is capable of organizing and facilitating. This is supported by evidence in prior studies, as well as actual implementation in certain large regional medical centers. Support from organizations that have used AI in this way is essential in guiding and integrating this technology on a broader scale.

The Future: A Global Blood Availability Database

One promising vision for the future is the creation of a global blood availability database, a platform that collects and shares real-time data on blood stocks, donations, and demand from across the world. Such a system would enable healthcare providers to allocate resources more efficiently, particularly in LMICs.

India’s e-RaktKosh system, despite its challenges, has proven that such a system is possible, even in regions with limited resources. By integrating AI into a global platform, experts hope to achieve more than just logistical efficiency. AI could enable predictive modeling on a global scale, identifying blood shortages before they occur by deploying targeted public awareness campaigns to boost donations.

AI could also help identify trends that human analysts might miss, such as seasonal donation patterns or regional variations in blood use. With this information, health authorities could better plan for blood shortages, ensuring that even in times of crisis, the most vulnerable populations have access to the blood they need.

AI for Social Good

The issue of blood availability deserves urgent attention. Blood is not just a resource for emergencies, it is a critical medicine, especially for pediatric surgeries and trauma cases. The integration of AI into blood availability systems could be the breakthrough needed to save countless lives.

While experts explore other high-tech solutions like drone deliveries or resource-intensive strategies such as walking blood banks, the healthcare community must first prioritize strengthening foundational data systems. By combining robust data collection with AI-driven analysis, we can build a future where no region is a “blood desert” and where everyone has access to the blood they need to survive.

Shreenik Kundu, MBBS, is a surgery fellow supported by the Jean-Martin Pediatric Global Surgery Fellowship at the Montreal Children’s Hospital Foundation. He is also a PhD scholar at McGill University in Montreal. Robert Glatter, MD, is an assistant professor of emergency medicine at the Zucker School of Medicine at Hofstra/Northwell, Lenox Hill Hospital, and Northwell Health in New York.