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Updated: Jul 17, 2026
A Precision Medicine Tool for Measurement and Monitoring of Hemoglobin S in Sickle Cell Disease Patients Receiving Transfusion Therapy
Artificial intelligence for anemia screening, diagnosis, and management
David B Olawade1, Yinka Julianah Adeniji2, Faithful A Olatunbosun3
1Department of Allied and Public Health, School of Health, Sport and Bioscience, University of East London, London, United Kingdom; Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom; Department of Business, Management and Health, York St John University, London, E14 2BA, United Kingdom.
Artificial intelligence (AI) offers a promising, non-invasive approach to anemia detection, especially in resource-limited areas. Further research is needed to address challenges for equitable global implementation.
Area of Science:
- Medical Informatics
- Public Health
- Biomedical Engineering
Background:
- Anemia impacts over 1.6 billion people globally, posing a significant public health challenge.
- Traditional anemia diagnostics are hindered by invasive procedures, personnel needs, and poor infrastructure in low- and middle-income countries.
Purpose of the Study:
- To review current literature on artificial intelligence (AI) applications for anemia screening, diagnosis, and management.
- To examine AI methodologies, performance, challenges, and future research directions in anemia care.
Main Methods:
- A comprehensive narrative synthesis was performed.
- Systematic searches of PubMed, IEEE Xplore, Scopus, and Web of Science databases were conducted.
- Additional hand-searching and expert consultation were utilized.
Main Results:
- AI models show variable accuracy (75-97% AUC) for anemia detection across diverse data.
- Machine learning algorithms demonstrate potential comparable to standard blood tests in research settings.
- Smartphone and point-of-care AI systems show promise for resource-limited settings, but real-world validation is limited.
Conclusions:
- AI holds significant potential to improve anemia care accessibility and efficiency.
- Addressing data standardization, algorithmic bias, regulatory issues, and equitable deployment is crucial for AI adoption in anemia management.
- Further validation in diverse populations and real-world settings is essential for widespread clinical use.
