Silent epidemic: AI may be key to tackling anemia in young women

Anemia, one of the most persistent global public health challenges, continues to shadow the health and economic prospects of millions of young women worldwide, cutting across geography, income levels, and social systems. Despite decades of nutritional programs and global health campaigns, prevalence rates remain stubbornly high, particularly among women of reproductive age.

In a new review published in Frontiers in Artificial Intelligence, researchers examine the complex web of causes behind anemia in young adult women and assess how artificial intelligence (AI) could reshape the way the condition is detected, managed, and prevented.

The study titled Anemia in young women: determinants and artificial intelligence-based management approaches conducts an in-depth analysis of the biological, socioeconomic, environmental, and genetic drivers of anemia, while mapping the rapidly evolving role of AI in screening, personalized treatment, and public health surveillance. The review argues that without technological innovation and systemic reform, global targets to significantly reduce anemia by 2030 are unlikely to be achieved.

Multifactorial roots of anemia in young women

Anemia in young adult women is rarely caused by a single factor. Instead, it reflects a layered interaction of nutritional deficiencies, physiological demands, chronic illnesses, genetic predispositions, socioeconomic disadvantage, and environmental conditions.

Iron deficiency remains the most prevalent cause globally. Iron is central to hemoglobin synthesis and red blood cell formation. However, deficiencies in folate, vitamin B12, vitamin A, and riboflavin also disrupt erythrocyte production. Heavy menstrual bleeding is a major physiological contributor, as monthly blood loss gradually depletes iron stores. Pregnancy intensifies iron requirements further, as maternal blood volume expands to support fetal development. When dietary intake does not keep pace with these demands, anemia develops.

Dietary patterns also influence risk. Plant-based diets that rely heavily on non-heme iron sources, which are less readily absorbed by the body, may contribute to iron deficiency when not properly balanced. In contrast, heme iron from animal sources is more efficiently absorbed. The complexity of nutrient interactions means that general dietary advice often fails to address individual absorption differences and metabolic variability.

Socioeconomic determinants deepen vulnerability. Low income restricts access to iron-rich foods and quality healthcare. Limited education reduces awareness of nutritional needs, especially during pregnancy. Rural residence often means reduced access to screening and early intervention services. The study highlights that women in low- and middle-income countries bear a disproportionate burden, with persistent prevalence rates that threaten to derail global nutrition targets.

Physiological and health factors compound the problem. Chronic inflammatory diseases can suppress bone marrow response and impair erythropoietin activity, leading to anemia of chronic disease. Infections such as malaria and parasitic infestations increase red blood cell destruction and nutrient loss. Genetic disorders such as sickle cell disease, sickle cell trait, thalassemia, and iron-refractory iron deficiency anemia introduce inherited vulnerabilities that require specialized diagnosis and management.

Environmental conditions also play a critical role. Limited access to clean water and sanitation increases exposure to infections that cause chronic inflammation and nutrient malabsorption. Broader environmental exposures, including harmful chemicals, may also influence hematologic health. The study presents anemia as a systemic issue embedded in structural inequality and biological complexity.

AI in early detection and personalized care

Against this backdrop of persistent prevalence and multifactorial causation, the authors argue that AI offers transformative potential. AI-driven systems are already reshaping diagnostic pathways, particularly in settings where traditional laboratory infrastructure is limited or costly.

Machine learning and deep learning models, including convolutional neural networks, support vector machines, naïve Bayes classifiers, random forest algorithms, and extreme learning machines, are being applied to detect and classify anemia with high levels of accuracy. AI can analyze complete blood count parameters to differentiate iron deficiency anemia from beta-thalassemia trait and hemoglobin variants, enabling more targeted interventions.

Non-invasive diagnostic methods represent one of the most promising developments. Smartphone-based applications that analyze images of fingernails or conjunctiva have demonstrated strong performance in estimating hemoglobin levels. These tools reduce the need for invasive blood draws and allow widespread screening, particularly in remote or resource-constrained regions. By lowering cost and logistical barriers, AI expands access to early detection.

AI also enhances dietary management. Advanced algorithms can process detailed health information, including eating habits, biometric data from wearable devices, genetic predispositions, and metabolic indicators. Using computer vision, AI systems can analyze food images to estimate nutritional intake more accurately than self-report methods. By integrating multiple data streams into comprehensive health models, AI enables dynamic, personalized nutritional recommendations that adapt to real-time changes in biomarkers or lifestyle.

Predictive analytics further strengthen prevention efforts. AI systems can identify emerging nutrient deficiencies before clinical symptoms appear, allowing timely dietary adjustments. Studies cited in the review indicate that AI-driven personalized nutrition interventions have led to measurable improvements in health indicators such as ferritin levels and overall metabolic health.

In clinical management, AI-based decision support tools such as the Anemia Control Model assist physicians in optimizing treatment regimens. By analyzing trends in hemoglobin, ferritin, transferrin saturation, and medication history, these systems recommend individualized dosing of iron supplements and erythropoiesis-stimulating agents. Evidence suggests that AI-guided management can improve hemoglobin stability, reduce medication overuse, and lower hospitalization risk, particularly in patients with chronic anemia.

Remote monitoring technologies expand this impact. AI-enabled wearables track physiological parameters such as heart rate, oxygen saturation, sleep patterns, and physical activity. Mobile health applications provide reminders, education, and real-time feedback, increasing patient engagement and adherence. These tools reduce dependence on frequent clinic visits and empower young women to manage their health proactively.

Public health surveillance, ethical challenges, and future directions

AI also holds promise in population-level health management. Predictive analytics can process large datasets to forecast anemia trends, identify high-risk regions, and guide targeted interventions. By integrating electronic health records, genomic information, and environmental data, AI enhances epidemiological mapping and resource allocation. Such capabilities are particularly valuable in low-resource settings where health systems must prioritize limited funding.

However, the integration of AI into anemia management is accompanied by serious ethical and logistical challenges. Algorithmic bias remains a pressing concern. Historically male-centered clinical datasets and limited representation of women in biomedical research may produce models that misdiagnose or underdiagnose female patients. Without deliberate diversification of training data and inclusive development teams, AI risks perpetuating existing health inequities.

Data privacy and security represent another critical barrier. AI systems rely on large volumes of sensitive health data, including reproductive information. Weak oversight, fragmented regulatory environments, and opaque data-sharing practices could undermine trust and hinder adoption. Transparent consent processes, robust encryption, and unified regulatory frameworks are essential safeguards.

Socioeconomic disparities create a digital divide. High implementation costs and limited digital literacy may prevent vulnerable populations from accessing AI-driven innovations. Without targeted policies to ensure affordability and inclusive design, AI could inadvertently widen health gaps.

Transparency and accountability also demand attention. Many AI algorithms function as black boxes, limiting clinicians' understanding of how decisions are generated. Clear standards for explainability, defined liability frameworks, and collaborative oversight among policymakers, healthcare providers, and technology developers are necessary to build confidence.

The study further calls for enhanced diagnostic precision through multimodal data integration. Combining imaging, genetic, clinical, and environmental data can create more robust models capable of distinguishing overlapping anemia subtypes. Predictive models for disease progression and personalized therapy optimization represent another frontier. Continuous real-world validation through clinical trials is essential to ensure reliability and safety.