Ai-Driven Predictive Tools in Hematological Disorders: A Comprehensive review of Models for Early Detection and Clinical Decision Support
DOI:
https://doi.org/10.29070/86mmzx53Keywords:
Artificial Intelligence, Hematological Disorders, Predictive Models, Early Detection, Clinical Decision SupportAbstract
There are still considerable problems to global health that are associated with haematological illnesses, such as thalassaemia, sickle cell disease, and haemophilia. This is especially true in places that have a limited diagnostic infrastructure. It is common for patients and healthcare systems to have significant problems as a result of delayed discovery and poor monitoring. These complications might include organ damage, higher mortality, and an increased financial burden. In recent years, Artificial Intelligence (AI) has emerged as a transformational tool in the field of predictive healthcare. It has opened up new pathways for early diagnosis, clinical decision support, and therapy optimisation. Identifying high-risk patients and predicting the course of disease may be accomplished through the use of AI-driven models, particularly those that are based on machine learning and deep learning techniques. These models are able to analyse complicated datasets that include imaging, laboratory biomarkers, and electronic medical records.
This paper examines the AI-based prediction tools used for haematological diseases critically, focussing on their role in early diagnosis and integration into clinical decision-making. This study synthesises data from secondary sources, including peer-reviewed research articles, clinical reports, and international health databases, to shine a light on the models' creation, architecture, and results. MRI T2* analysis for iron overload in Thalassaemia is one example of an AI application that uses imaging and has demonstrated remarkable diagnosis accuracy. In contrast, multimodal and lab-based models that rely on common biomarkers have demonstrated potential in low-resource settings for producing scalable and economical solutions.
The paper also explores the opportunities and challenges in adopting AI in urban healthcare systems, emphasizing issues such as infrastructure limitations, data fragmentation, ethical concerns, and the absence of comprehensive regulatory frameworks. In order to help healthcare practitioners, lawmakers, and academics understand how AI might improve predictive healthcare and lessen the burden of disease, this paper uses secondary data to offer evidence-based ideas. To completely incorporate AI into haematology and develop sustainable, patient-centered healthcare models, the results highlight the need of focused investments, ethical standards, and ongoing innovation.
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