Artificial Intelligence in Predictive Healthcare: A Review of Non-Invasive Solutions for Iron Overload Management in Thalassemia Patients
DOI:
https://doi.org/10.29070/wcfgad82Keywords:
Artificial Intelligence, Predictive Healthcare, Thalassemia, Iron Overload, Machine LearningAbstract
In particular, developing nations like India have a significant problem in terms of public health because of the prevalence of thalassaemia, a hereditary blood illness that is rather common. In spite of the fact that regular blood transfusions are necessary for the survival of the patient, they can cause iron overload, which can have a devastating impact on important organs including the heart, liver, and endocrine system. Numerous non-invasive diagnostic techniques, such as magnetic resonance imaging (MRI) T2 imaging and serum ferritin assessments, are utilised extensively; however, their utilisation is restricted in urban healthcare settings due to the restrictions of cost, accessibility, and infrastructure. The most recent developments in artificial intelligence (AI), in particular machine learning and deep learning algorithms, have shown that they have enormous promise for use in predictive healthcare applications. The processing of massive secondary datasets by these technologies allows for the identification of early risk indicators, the prediction of problems, and the optimisation of personalised care programs targeted towards Thalassaemia patients.
This paper presents a comprehensive review of AI-driven non-invasive solutions for predicting iron overload in Thalassemia patients, with a focus on leveraging secondary data sources. In it, the most relevant models and technologies used in the therapy of haematological illnesses are examined in depth, along with a thorough literature review on AI in healthcare. Furthermore, the study delves into the challenges and limitations linked to AI applications. Some of these issues include the lack of standardised frameworks in urban healthcare systems, ethical considerations, algorithmic biases, and data privacy. The paper also discusses the potential of using AI-powered prediction tools in routine healthcare settings. Early diagnosis, reduced healthcare expenses, and improved patient outcomes are all possible outcomes of this.
A organised method to bridging the gap between artificial intelligence innovation and practical healthcare delivery is provided by this review. This approach is achieved by synthesising ideas from previously conducted research and reports. It is anticipated that the findings will provide healthcare practitioners, policymakers, and researchers with information regarding the transformational potential of artificial intelligence in predictive healthcare, which will eventually contribute to the sustainable treatment of Thalassaemia and other chronic illnesses
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References
1. Abedi, I., & Zamanian, M. (2023). Cardiac and hepatic T2* MRI dataset for iron overload classification in thalassemia patients. Journal of Medical Imaging Analytics, 5(1), 67–79.
2. Ahmed, H., & Lewis, J. (2023). Predictive modeling in healthcare: machine learning approaches and clinical relevance. Journal of Medical Informatics, 18(1), 22–38.
3. Asmarian, N., Kamalipour, A., & Haghpanah, S. (2022). Machine learning prediction of heart and liver iron overload in β thalassemia major patients. Hemoglobin, 46(6), 303–307.
4. Basu, S., & Malik, R. (2023). Patterns of organ damage due to iron overload in transfusion-dependent patients. Hemoglobinology Review, 15(2), 134–149.
5. Basu, S., & Shankar, V. (2023). Pathophysiology and multisystem complications of iron overload in transfusion dependent thalassemia. Hematology Insights, 8(1), 45–59.
6. Brown, L., & Chen, D. (2023). Global epidemiology of beta-thalassemia and carrier prevalence. International Journal of Hematology, 12(1), 45–58.
7. Chakraborty, A., & Ramesh, V. (2023). Evaluating digital infrastructure gaps in India’s urban healthcare. Journal of Health Systems Research, 12(3), 144–158.
8. Chinnaiyan, S., & Sharma, L. (2024). Quality of life and burden of thalassemia in Indian urban contexts. Journal of Public Health Practice, 18(4), 299–310.
9. Das, S., & Bhattacharya, P. (2023). Challenges of data quality for AI implementation in Indian clinical settings. Asian Journal of Medical Informatics, 9(2), 45–57.
10. Elkalioubie, M., & Omar, H. (2025). Correlation of MRI T2*, serum ferritin and elastography in detecting hepatic iron overload in pediatric β thalassemia. Journal of Pediatric Radiology, 10(2), 101–109.
11. Farooq, M. S., & Younas, H. A. (2023). Federated learning enabled Thalassemia carrier detection using complete blood count indices. Journal of Medical AI Systems, 4(2), 102–110.
12. Ferih, K., & Deshpande, S. (2023). AI based automated MRI quantification for liver iron overload: a validation. Medical Imaging Review, 11(2), 89–98.
13. Ferih, K., & Kumar, P. (2023). Applications of artificial intelligence in thalassemia diagnosis. AI in Medicine Journal, 9(1), 112–125.
14. Fernandez, R., & Kumar, P. (2024). Algorithmic fairness and population diversity in AI driven healthcare. International Journal of AI in Medicine, 15(1), 88–103.
15. Fu, C., & Yang, X. (2025). Cardiac injury mechanisms caused by iron overload in thalassemia. Frontiers in Pediatrics, 1514722.
16. Gunčar, G., & Notar, M. (2024). Machine learning applications for hematological diagnosis based on blood tests. Machine Learning in Health, 7(3), 89–102.
17. Iyer, K., & Pathak, S. (2024). Electronic health record integration and barriers to AI readiness in metropolitan hospitals. Urban Health Technology Review, 8(1), 23–37.
18. Jackson, L. H., & Fernandez, J. P. (2017). Non invasive MRI biomarkers for early assessment of iron induced liver injury. Scientific Reports, 7(1), 43439.
19. Jones, A., & Rao, M. (2024). β-Thalassemia mutations and prevalence in India. Blood Disorders Review, 5(2), 67–83.
20. Karim, S., & Zhou, L. (2023). Deep learning based anemia detection using peripheral blood smear imaging. AI in Laboratory Medicine, 6(2), 89–96.
21. Kell, D. B., & Richards, G. (2014). Serum ferritin as a marker for iron overload: limitations in inflammatory states. Metallomics, 6(4), 748–760.
22. Krishnan, N., & Lal, D. (2024). Regulatory readiness for clinical AI tools in Indian healthcare. Journal of Health Policy and Governance, 7(1), 67–80.
23. Kumar, A., & Lee, P. (2024). Balancing accuracy and accessibility: AI tools in low resource healthcare settings. Global Health Technology Review, 9(1), 110–123.
24. Kumar, A., & Zaveri, R. (2024). National carrier prevalence estimates of β thalassemia in India: A 2023 update. Genetic Epidemiology Journal, 12(3), 123–134.
25. Mehra, Y., & Dutt, S. (2023). Legal and ethical implications of predictive AI in medical diagnostics. Indian Journal of Medical Ethics, 18(2), 34–46.
26. Mehta, R., & Aggarwal, S. (2025). Systemic manifestations and complications of iron overload in β thalassemia patients. Clinical Hematology Journal, 14(1), 64–78.
27. Miles, G., & Fernández, A. (2024). Machine learning in outpatient anemia diagnosis: feasibility study. Hematology Care Journal, 11(3), 56–68.
28. Mohsen, F., & Hajj, N. (2022). Fusion of electronic health records and medical imaging using AI. Journal of Biomedical Informatics, 24(5), 201–215.
29. Mukherjee, R., & D’Costa, J. (2023). Data privacy and cybersecurity concerns in emerging clinical AI platforms. Clinical Informatics in Practice, 10(2), 76–89.
30. Musallam, K., & Singh, V. (2024). Serum ferritin limitations in non-invasive diagnostics of iron overload. Clinical Hematology Insights, 11(3), 76–88.
31. Nashwan, A. J., & Alkhawaldeh, I. M. (2023). Using AI to improve body iron quantification: A scoping review. Blood Reviews, 62, 101133.
32. Patel, D., & Gupta, N. (2023). Global distribution and burden of thalassemia: patterns and determinants. Journal of Hemoglobin Disorders, 9(2), 98–112.
33. Patel, R., & Gupta, S. (2023). Carrier rates and genetic screening of Thalassemia in urban India. Journal of Genetic Counselling, 10(1), 34–50.
34. Pérez, R., & Lim, T. (2023). Assessment of iron overload using deep learning and MRI T2* data. Journal of Radiological AI, 7(4), 203–214.
35. Pinto, S., & Varghese, L. (2024). Urban public hospitals and the digital divide in AI adoption. Healthcare Management Perspectives, 6(1), 102–118.
36. Positano, V., & Giordano, R. (2023). Deep learning classification of liver iron concentration from multiecho T2* MRI. Journal of Magnetic Resonance Imaging, 57(3), 451–460
37. Rao, E., & Desai, P. (2024). Economic and social burden of thalassemia in India. Public Health Economics Review, 6(2), 120–135.
38. Rao, E., & Kapoor, H. (2024). β Thalassemia epidemiology in India: geographic and demographic trends. Indian Journal of Hematological Research, 7(4), 201–215.
39. Rezaei Kalantari, K., & Haddad, M. (2024). Longitudinal MRI assessment of myocardial and hepatic iron clearance in β thalassemia major. Tehran Heart Center Journal, 5(2), 75–84.
40. Singh, N., & Tan, H. (2025). NLP supported early detection of hematological malignancies: multiple myeloma insights. Journal of Clinical AI, 12(1), 33–45.
41. Smith, J., & Kapoor, H. (2024). Global burden of thalassemia: incidence and mortality trends. Journal of Global Health, 13(1), 25–36.
42. Smith, J., & Patel, H. (2024). Global incidence and mortality burden of thalassemia: recent trends and projections. International Journal of Genetic Disorders, 5(1), 30–42.
43. Taher, A. T., & Amin, R. (2025). Iron overload in thalassemia: organ specific complications and monitoring limitations. Hematology Reviews, 16(1), 22–35.
44. Vora, H., & Sinha, M. (2023). Bias and interpretability challenges in healthcare AI applications. Journal of Computational Medicine, 11(3), 120–135
45. Youssef, O., & Chen, Y. (2022). Predictive analytics in sickle cell disease: real world machine learning applications. Blood Disorders Analytics, 4(1), 67–79.
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