Anemia affects people of all ages and social backgrounds and it is a major public health problem worldwide. Anemia is most prevalent in developing countries (Behera et al., 2024). Majority of the women and children are disproportionately affected by anemic condition worldwide, making them the most vulnerable groups. 2019 statistics showing that an approximately 30% of women aged 15 to 49 years were anemic. In 2023 there is no improvement in the anemic percentage of non-pregnant and pregnant women 15–49 years (WHO, 2025). It clearly indicates that approximately half a billion women of reproductive age are anemic worldwide (Merid et al., 2023). But the impact of anemia is different, the burden is greatest in low and lower- middle -income countries (LMICs), where the condition is more common in rural areas, among low- income households, and among populations with less access to formal education (WHO, 2025). This is a significant socioeconomic and developmental barrier, as evidenced by its persistently high prevalence, particularly in LMICs and among vulnerable population. It slows the development of human capital and starts a cycle of sickness and poverty. When women are overloaded with work, caring for their families, or participating in education and decision- making, the negative impact is generational, reducing productivity, educational attainment, and cognitive ability (Merid et al., 2023). There is an urgent need to reassess and potentially disrupt strategies, as current global efforts are not enough to achieve the 2030 targrt of a 50% reduction in the prevalence of anemia (WHO, 2024).

Artificial intelligence (AI) is transforming healthcare rapidly, offering revolutionary opportunities for disease prediction, early diagnosis, and preventative treatment. By identifying complex patterns in large medical date sets that humans cannot see or predict, AI dramatically improves diagnostic accuracy through advanced machine learning (ML) algorithms, deep learning networks (DL), and advanced big date analytic (Ahmed et al., 2020). Convolutional neural networks (CNNs), Recurrent Neutral Networks (RNNs) and Support Vector Machines (SVMs) are examples of AI models that have established significant success in improving early diagnosis rates for numerous diseases, including diabetes, heart disease, and certain types of cancer (Khalifa and Albadawy, 2024).

Beyond diagnosis AI has enormous potential to improve patient care efficiency, streamline treatment planning, and reduce overall healthcare costs (One Sec Magazine, 2025). A new way to overcome the inherent shortcomings of tradition anemia diagnosis and treatment is AI’s advanced data analysis and patter recognition capabilities. Traditional approaches to disease diagnosis often rely on subjective assessment and late onset of symptoms, leading to delays in treatment and high mortality rates (Khalifa and Albadawy, 2024). Furthermore, traditional anemia diagnosis can be invasive, time- consuming, and inconvenient for patients (Sehar et al., 2025). However, the ability of AI to process and understand large and complex data sets creates opportunities for earlier, more accurate, and more personalized interventions, especially in settings with limited access to traditional diagnostic infrastructure. This stark contrast demonstrates AI’s potential to address and even eliminate the current errors and mistakes in the treatment of anemia (Khalifa and Albadawy, 2024).

In the context of present study “young adult women” are basically considered as those aged 18 to 26 years, although some of them, due to their better development, fall into the age group of 16 to 30 years (NAHIC, 2025). People usually become more independent, develop lifelong health habits, and take on adult responsibilities during this age range which is acknowledged as a crucial transitional period (Science Focus, 2025). According to society, 18 is the “age of majority” and 26 is frequently mentioned as the age at which many people have finished the normal adult transitions and are becoming established as adults (NAHIC, 2025).

Conditions like mental disorders and sexual health problems, which include the highest rate of unplanned pregnancy among women aged 18 to 24. Often start in young adulthood Significant socioeconomic vulnerabilities also define group; for example, 74% of 18 to 25 years- olds make less than $25,000 annually, which can have a negative impact on their long—term health (Science Focus, 2025). Together, these elements highlight the significance of targeted health interventions for young adult women, a demographic that is both physiologically vulnerable to anemia and frequently encounters structural obstacles to receiving healthcare and achieving the best possible health outcomes (Kumar et al., 2022).

Present review aims to provide an overview of the current knowledge on anemia in young women. Information is particularly relevant for women aged 15–49 years represent the age group with the highest risk of anemia. To fully understand the potential of artificial intelligence to promote health equity, this review article aims to assess the effectiveness of current applications of AI, analyze the main limitations and ethical issues associated with their use, and suggest future directions for research and policy action.

A decrease in the red blood cells number or a lower-than-normal concentration of hemoglobin (Hb) is the two main characteristics of anemia, several physiological consequences result from this deficiency, affecting the blood’s ability to effectively oxygen to the body’s cells and organs (WHO, 2025).

The World Health Orgaization (WHO) states that certain hemoglobin thresholds are used to diagnose anemia. Anemia is defined as a hemoglobin level below 12.0 g/dL in non-pregnant women and below 11.0 g/di in pregnant women (Behera et al., 2024). Table 1 shows the WHO/CDC classification and diagnostic criteria for anemia in women. Different are used to further classify the severity of anemia:

Mild anemia by an Hb level of 11.0 to 11.9 g/dL in non-pregnant women and 10.0 to 10.9 g/dL in pregnant women. Moderate anemia by hemoglobin levels of 8.0 to 10.9 g/ dL in non-pregnant women and 7.0 to 9.9 g/dL in pregnant women. Severe anemia by hemoglobin levels fall below 8.0 g/ dL in non-pregnant women and below 7.0 g/dL in pregnant women (Children's National, 2025).

It is important to understand that a person’s sex, ethnicity, and physiological state can all have a substantial impact on their typical hemoglobin distribution (Behera et al., 2024). For instance, because the oxygen partial pressure is lower at higher elevations residents need to have their Hb cutoffs adjusted upward (Children's National, 2025). A thorough evaluation that takes into account hematologic markers, knowledge of the underlying pathogenic mechanisms, and a thorough patient history is necessary for the diagnosis of anemia, which is frequently multifactorial (Behera et al., 2024). Because a complete blood count (CBC) may not be able to identify iron deficiency in its early stages, a second blood test to quantify ferritin protein is often necessary for illnesses such as iron insufficiency (Channar et al., 2023). Given the complex nature of anemia and the variation in Hb cutoffs depending on altitude, ethnicity, and phycological Status (pregnancy), comprehensive and context- specific diagnostic methods are essential. The limits of generic cutoffs or subjective clinical judgments may be overcome by AI, which has the potential to do this by combining numerous data points beyond simple Hb levels and providing a more relevent, precise, and context—aware diagnostic assessment (Pawuś et al., 2024).

As previously mentioned, 30% of women worldwide between the ages of 15 and 49 years suffered from anemia in 2019 (WHO, 2025). It was slightly more common by 2023, affecting 35.5 and 30.7% pregnant and non-pregnant women in 15 to 49 years age range (WHO, 2024).

Low- and middle—income countries (LMICs), particularly those in the WHO regions of Africa and South- East Asia, bear the greatest burden of potentially severe anemia. An estimated 106 million women in Africa and 244 million in South—East Asis are anemic, the highest percentage of women living in these regions alone (WHO, 2025).

Despite of continued international efforts and initiatives, the prevalence of anemia remains high. The global target of reducing anemia by 50% 2030 is far from being achieved, and there are significant barriers to achieving the Global Nutrition Target (GNT), which calls for a 50% reduction in anemia among women of reproductive age (WHO, 2024). Theis high incidence, which continues over time, highlights the limitations of current interventions and the urgent need for creative, scalable solution. The high prevalence of chronic anemia reflects the fact that current public health strategies, although valuable are not sufficient to prevent this disease. AI technologies and its implementation and usage will improve the global health as revolutionary way.

Anemia can cause a number of symptoms that severely reduce a person’s productivity and overall well- being, Extreme fatigue, decreased physical performance, shortness of breath, dizziness or fainting, cold hands and feet, headaches, and flushing of the skin or mucous membranes are common health effects. In more extreme situations, symptoms, may worsen and include elevated bruising, fast heartbeat, and quick breathing (WHO, 2025). Neurological symptoms, such as numbness, muscle weakness, psychological problems (from mild depression to confusion and dementia), balance and coordination issues, and pins and needles, are also noted for certain types of anemia, such as those brought on by vitamin B12 or folate deficiency (Franciscan Health, 2025). Anemia effects are not limited to the individual: in children severe anemia can hinder cognitive and motor development and in pregnant women increase the risk of birth difficulties, which can result in low-birth-weight newborns, delayed development, and compromised immune systems in their offspring (WHO, 2025).

A harmful intergenerational cycle is produced by the severe health effects of anemia, especially on physical and cognitive abilities. Particularly iron deficiency in women are greatly hindered from realizing their full potential by anemia, which causes persistent fatigue, compromised immunity, and diminished cognitive function (One Sec Magazine, 2025). Women who are too worn out to work, take care of their families, or engage in education and decision—making have a direct impact on the welfare of their households and the larger development of their communities’ women are the main contributors to productivity in many economies, especially those that depend on agriculture and caregiving. Communities suffer as a result of general productivity declines brought on by iron deficiency (One Sec Magazine, 2025). Especially, anemia in young women impairs cognitive function, labor productivity, educational attainment, and mental wellness (Merid et al., 2023). Theis illustrates a definite cause—and—effect link in which a health condition has a direct impact on a person’s potential, the well- being of their family, and the growth of the community as a whole, resulting in a vicious cycle of poverty and bad health outcomes that persists for generations. Therefore, treating anemia is not just a medical procedure but also a vital investment in the advancement of society and individuals.

Iron deficiency anemia most prevalent in worldwide (WHO, 2025). The pathogenesis, of iron functions in hemoglobin synthesis and erythrocyte formation, deficits in other essential micronutrients; including vitamin A, Folate, vitamin B12, and riboflavin, also play important roles (WHO, 2025). Number of factors are causing for low iron levels in young women. Because the monthly blood loss severely, depletes iron stores, heavy menstrual bleeding is recognized as a major physiological risk factor, especially in younger women (Kario and Kurniawan, 2024). In the pregnancy time significant raise of iron was observed, due to the increase of the red blood cell volume in younger fetus (Georgieff, 2020). Anemia and a higher chance of unfavorable delivery outcomes, such as low birth weight babies, result when this increased demand frequently exceeds nutritional intake (Merid et al., 2023). Moreover, one common dietary reason, vegetarians and vegans are more likely to get iron deficiency (Kario and Kurniawan, 2024) which comes from plant sources including spinach, beans, legumes, nuts and fortified cereals, because these are less accessible non-heme iron sources. Where as in animal sources like meat and poultry contains heme iron which is more easily absorbed by the body (Kario and Kurniawan, 2024).

An effective dietary interventions are essential. For prevention and management of nutritional anemia. This involves eating a healthy balanced, diet that includes foods which are high in iron folate, vitamin B12, and Dietary supplements may recommend (WHO, 2025). The difference of heme and non- heme iron absorption is the primary cause for anemia and the need for other micronutrients underscore the challenge of successfully implementing dietary therapy (Soni et al., 2025). AI can play a main role in providing personalized individual dietary recommendations that go beyond general recommendations to address specific deficiencies related to each individual’s it maximize the nutrient absorption based on individual needs. AI can optimize nutrient intake and address specific deficiencies than tradition general nutritional advice by analyzing an individual’s unique dietary habits, genetic predispositions, metabolism, and other heath data to provide personalized recommendations that take into account (NHS Inform, 2025).

The anemia prevalence is significantly influenced by socioeconomic status. It includes Low income, limited access to health services, living in rural areas, and educational illetarcy are important factors that strongly contribute to an increased risk of anemia (DHS Program, 2025). The risk of anemia is increased by low educational attainment, with a poor understanding of the importance of nutrition, especially during pregnancy (Davidson et al., 2022). The prevalence of anemia is also driven by the lack of adequate social support unstable employment or low household income, which further increasing the risk of anemia (Davidson et al., 2022).

Directly linked with access to health foods and high- quality health care, especially basic prenatal care, low socioeconomic status of individuals and families are (Sehar et al., 2025).

These socioeconomic conditions create a complex web of disadvantage. Understanding this broader problem is crucial, as it suggests that interventions must be multifaceted and address not only medical, but also socioeconomic factors (Davidson et al., 2022). This ins’ t merely a collection of isolated factors—it is a systemic issue where one problem, like poor health, can trigger a chain reaction, limiting access to essential needs such as nutritious food and a health way of life. This leads to a vicious cycle of vulnerability to anemia.

Young women is a significantly higher risk of anemia due to unique biological vulnerabilities, which are further face a compounded by chronic illnesses and genetic predispositions.

Environmental factors and socioeconomic conditions, they are directly linked to influence the incidence of anemia. These include elements of the physical environment and broader social factors that influence health.

Access to clean water, hygiene and sanitation (WASH) is essential. Particularly among young women insufficient supplies of safe drinking water and inadequate sanitation are associated with a higher risk of anemia (Merid et al., 2023). Inadequate WASH (Water, Sanitation, and Hygiene) facilities contribute to the spread of infection diseases—including parasitic infection like schistosomiasis and soil—transmitted helminths, as well as diarrheal illnesses—which can cause chronic blood loss, inflammation, and nutrient malabsorption, ultimately increasing the risk of anemia. Prevention of anemia requires immediate attention on addressing these environmental factors in addition to poverty and illiteracy (WHO, 2025).

The impact of environmental inequalities is reflected in the global prevalence of anemia, which is more common in rural areas and among low-income families (WHO, 2024). The risk of anemia can also be influenced by the general environment, including exposure to harmful chemicals and living can be affected by a wide range of environmental influences (Munro, 2023).

Artificial intelligence plays a transformative role in improving diagnostic accuracy by identifying complex patterns using large medical datasets (Khalifa and Albadawy, 2024). Several machine learning (ML) and deep learning (DL) algorithms, including convolutional neural networks (CNN), support vector machines (SVM), naïve Bayesian systems, XGBoost, Random Forest, and Extreme Learning Machines (ELM), are being developed and applied for early diagnosis and classification of various types of anemia (Khalifa and Albadawy, 2024).

Through various non-invasive diagnostic methods a significant progress is made. These methods have been shown to be effective, cheaper, and faster than traditional invasive blood tests they are conjunctival, palm or nail image analysis (Subramanian et al., 2020). In particular, AI- powered smartphone applications that can estimate hemoglobin (Hb) levels via “nail selfies” with high accuracy show a mean absolute error of = 0.7 g/dL, improving to = 0.50 g/Dl, for Hb levels above 10 g/Dl (Mannino et al., 2025). These smart apps enable convenient self-monitoring and have been widely used in the real world with over 1.4 million tests prevalence by over 200,000 users, facilitating the first county—level mapping of anemia prevalence in the United States (Mannino et al., 2025). The use of personalized apps for patients with chronic anemia has further improved diagnostic accuracy by nearly 50% (Chapman University, 2025).

Due to their high accuracy non-invasive nature, AI—driven diagnostic tools, particularly smartphone—based solutions, have proven to be effective and affordable screening methods. Traditional diagnostic infrastructure is limited and expensive than the detection through AI, and also this is less expensive, shorter in duration, and reliable for early detection (Subramanian et al., 2020). The high accuracy of these non-invasive methods has been consistently reported, with CNN achieving 90.27% accuracy and ELM 99.21% accuracy for anemia detection, further supporting their practical utility and significant potential for broad public health impact (Subramanian et al., 2020).

Furthermore, AI models can analyze complete blood counts (CBCs) to diagnose and classify specific types of anemia, such as iron deficiency anemia (IDA), beta- thalassemia trait (BTT), and hemoglobin E (HbE), with ELM models achieving up to 99.21% accuracy (Sehar et al., 2025). Several high-throughput machine learning models that can not only detect the presence of anemia but also classify specific types demonstrate that AI can significantly improve diagnostic accuracy. This goes beyond simple detection and resource allocation. Integrating ontological knowledge with machine learning models has also been suggested to further improve classification results and increase the interpretability of AI decisions (Sehar et al., 2025).

Artificial intelligence and machine learning (AI) can be used to provide personalized nutritional advice and develop advanced strategies. These systems utilize personalized health information, including eating habits and even genetic data, to inform care and treatment decisions (NHS Inform, 2025). AI algorithms analyze large amounts of data to identify very specific nutritional deficiencies and then recommend personalized nutritional programs to address these imbalances (NHS Inform, 2025).

The integration of artificial intelligence into chronic disease management has creates opportunities to improve the efficiency of patient care and patient care and optimize various treatment strategies (Pan et al., 2025). Specific AI-driven decision support systems, such as the Anemia Control Model (ACM), have been developed to help physician’s select personalized anemia treatments for patients (Gandjour et al., 2025).

AI offers unparalleled opportunities to improve public health outcomes by identifying risk factors detecting patterns and predicting outbreaks to processing massive amounts of data (Paramasivan, 2023). This kind of approach helps in risk assessment, particularly valuable for managing and preventing anemia at a population level.

AI systems, despite of their perceived objectivity they are inherently reflections of the data they are trained on and the biases embedded in their design processes (Most, 2025). Historically, a significant biomedical research and clinical trials have been male—centric by leading on the other hand an under representation of women in the research datasets (Most, 2025; Karpel et al., 2025). The data imbalance results in AI models that may misdiagnose, underdiagnose, or be significantly less accurate when applied to female patients as diseases can present differently in women compared to men (Most, 2025). The lack of diversity within the AI development teams further contributes to blind spots and limits the perspectives informing model design and evaluation (Most, 2025). Even generative AI is trained on vast online content that can perpetuate and amplify cultural stereotypes (Most, 2025).

Differences in the inherent biases in historical medical data, particularly the male—centric focus and women’s under—representation, constitute a significant threat to egalitarian AI application; without purposeful and continual efforts to diversify training data and development teams, AI runs the risk of not merely reproducing but actively amplifying existing health inequities for young adult women, limiting its potential for beneficial effect. This algorithmic bias can increase existing health disparities, particularly among marginalized populations such as Black, Indigenous, and other communities of colour women, LGBTQ+ communities, and people with disabilities (Accuray, 2025).

To decrease algorithmic bias for mitigation strategies include implementing comprehensive data collection practices to ensure diverse demographic representation, ongoing monitoring and review of AI results to identify and remove bias early, supporting multidisciplinary development teams, and actively involving representatives of disadvantaged populations in the design and evaluation process (Accuray, 2025).

Patient data protection is a major concern for AI technologies in healthcare, as these technologies rely on large amounts of medical data (Khalifa and Albadawy, 2024). Misuse of data (especially when transferred between institutions without sufficient oversight), significant risks include unauthorized access, data breaches, and vulnerabilities associated with cloud—based AI application (Alation, 2025). Concerns are particularly heightened for women’s reproductive health apps, where sensitive user information is subject to evolving governmental regulations and varying state—lavel laws, posing risks of privacy breaches and misuse (Zadushlivy et al., 2025). Lack of user transparency and protection regarding how data is stored and shared with third parties, even for marketing or analytical purposes (Zadushlivy et al., 2025). Consumer surveys consistently reveal high levels of concern about online privacy with a majority are believing AI poses a significant to their personal information (Puntoni et al., 2021).

The extensive collection of sensitive health data by AI applications, combined with a persistent lack of transparency regarding data usage and evolving, fragmented regulatory landscapes, creates significant privacy risks that can influence user trust and hinder widespread adoption (Williamson and Prybutok, 2024). This is mainly relevant for young women who share highly personal and sensitive information. For mitigation strategies minimizing data collection, collecting only what is essential, establishing clear and comprehensive consent processes: and developing strong, unified regulatory frameworks will control this (Alation, 2025). This implies that technological advancement in AI is not accompanied by commensurate ethical safeguards and it required clear, adaptable regulatory frameworks, for public resistance and ultimately full potential in improving health outcomes.

Despite AI’s potential to improve access to healthcare, its actual uptake in healthcare has been slower than anticipated, partly due to high implementation costs and limited compatibility with existing hospital infrastructures (Shah, 2024). Pre-existing socioeconomic disparities, including limited digital literacy and unequal access to technology, create a new form of “digital divide” that could prevent vulnerable young women, who often bear the highest burden of anemia, form fully benefiting from this innovation (Alarsaheb, 2025; Al-Khassawneh, 2023).

Socioeconomic factors, like income levels and educational attainment will significantly influence both access and the effective adoption of digital health technologies (Davidson et al., 2022). The “digital divide” remains a critical barrier; ensuring affordable digital health technologies and widespread digital literacy programs are crucial for equitable access (de Graaf et al., 2025). Initiatives of digital literacy are identified as promising interventions to empower women to engage critically with AI technologies, understand their implications, and help mitigate gender disparities in the digital health space (Karpel et al., 2025). Without these primary efforts, AI could inadvertently exacerbate existing health inequalities by primarily benefiting those who already possess the means to access and effectively utilize the technology, thus failing to impact the most vulnerable.

Many AI algorithms operate as “black boxes” making it difficult for users to understand, how decisions or recommendations are generated including healthcare professionals and patients (Alation, 2025). This opacity inherently undermines trust. Patient in AI in healthcare is further hindered by fears of diagnostic errors or device malfunctions, the lack of transparency in AI decision—making, and significant concerns about data privacy and unauthorized sharing with third parties (Alation, 2025).

Establishment of comprehensive regulatory frameworks which leads to global fragmentation and inconsistent laws that create in compliance and oversight of AI technology (Alation, 2025). The “black-box “nature of many AI algorithms and the lagging, fragmented regulatory frameworks critical governance gap threatening patient trust and hindering ethical, widespread adoption (Lin, 2024). This regulatory vacuum implies a lack of clear accountability and standardized safety and efficacy measures. There is an urgent need for collaborative oversight involving policymakers, healthcare professionals, and tech developers to ensure responsible innovation (Alation, 2025). Accountability for the performance and outcomes of AI algorithms must be clearly defined and enforced (Vij, 2024). Without robust, proactive governance including clear consent processes, bias mitigation, and defined responsibilities are AI’s potential benefits will be undermined by legitimate concerns about safety, fairness, and accountability, particularly when dealing with sensitive health data and vulnerable population like young women (Emma, 2024).

Significant logistical challenges in AI is follows as healthcare IT infrastructure, clinical workflows, and administrative processes also presents (Wright and Fairley, 2025). Like Seamless interoperability with electronic health records (EHRs), imaging equipment, and other healthcare technologies are essential to avoid disruptions and inefficiencies (Donins and Behmane, 2023). The lack of validation methods also limits the AI’s acceptance in medical practice (Wright and Fairley, 2025).

Interdisciplinary collaboration is required for successful integration among clinical, IT, and AI teams (Wright and Fairley, 2025). By using interoperability standards and open APIs can facilitate compactivity (Hattab et al., 2025). Hence, the rapid pace of technological advancements often outpaces the development of regulates various frameworks and practical integration strategies by creating implementation gaps (Alation, 2025).

Ethical implications are involved in AI in healthcare extended beyond the data privacy and bias to encompass issues of accountability, informed consent and the nature of human-AI collaboration (Gifari et al., 2021).

Even though AI technology supports healthcare services, excessive dependence on AI technology may devalue human judgment and cause errors to be overlooked with potentially fatal consequences (Bouderhem, 2024). Untrustworthy predictions with AI systems, especially large language models, can generate “hallucinations” (Yu et al., 2024).

With a collaborative approach AI integration should utilize AI as a supplement to human to human—led practices rather than a replacement (Bharel et al., 2024). Human-in—the -loop processes are a vital step toward increased accuracy and trust in AI—where technology is constantly refined by human oversight. This cycle of trustworthiness contributes to improved system reliability, reduction of errors, and fostering of trust which in term fosters AI transparency and acceptance among healthcare workers (Hattab et al., 2025).

The main goal is a balance between the complexity of AI algorithms and the need for transparency by ensuring that AI tools positively on doctor and patient relationships by fostering empathy, shared decision—making and trust (Nasir et al., 2025).

Future research and development on arterial interleave to combat anemia should on several key areas as follows.

AI can help solve many important problems in women’s health, especially in the context of anemia.