| Sumario: | Simple Summary: This paper aims to highlight the latest advancements in the application of artificial intelligence in the diagnosis of myelodysplastic syndrome. This research focuses on a group of blood disorders called Myelodysplastic Syndrome (MDS), which can potentially develop into a more severe condition called Acute Myeloid Leukemia (AML). Detecting MDS early is crucial, but the current methods are time-consuming and labor-intensive. We aim to explore how artificial intelligence (AI) and machine learning (ML) can make the diagnosis of MDS faster and more accurate. AI involves computer programs that can think like humans, and ML is a part of AI that helps computers learn patterns and make predictions. By using these technologies, doctors can improve how they diagnose MDS, leading to better treatment and outcomes for patients. Myelodysplastic syndrome (MDS) is composed of diverse hematological malignancies caused by dysfunctional stem cells, leading to abnormal hematopoiesis and cytopenia. Approximately 30% of MDS cases progress to acute myeloid leukemia (AML), a more aggressive disease. Early detection is crucial to intervene before MDS progresses to AML. The current diagnostic process for MDS involves analyzing peripheral blood smear (PBS), bone marrow sample (BMS), and flow cytometry (FC) data, along with clinical patient information, which is labor-intensive and time-consuming. Recent advancements in machine learning offer an opportunity for faster, automated, and accurate diagnosis of MDS. In this review, we aim to provide an overview of the current applications of AI in the diagnosis of MDS and highlight their advantages, disadvantages, and performance metrics.
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