Artificial Intelligence in Predictive Healthcare: A Review of Non-Invasive Solutions for Iron Overload Management in Thalassemia Patients

Abstract

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

Ähnliche Arbeiten