Exploring Machine Learning and Artificial Intelligence for Sickle Cell Anemia Research: A VOSviewer-Based Bibliometric Analysis

Abstract From the Scopus database,around 36,592 records related to Sickle cell anemia were collected .These 36,592 papers were trimmed down to 7,438,where all the selected papers are open-access papers and are published between 2020 and 2024.This is done for the reason that this research would resonate with current research trends.Furthermore,using filters such as type of article, current stage of publication, the origin of papers’ contributions, relevant keywords, and publishing language,144 papers were selected and used for the bibliometric Analysis.Though these 144 is a small collection of papers, it balances the depth and manageability in our bibliomet-ric analysis while still resonating with current research trends.VOSviewer-Visualizing scientific landscapes, is used for the visualization and the bibliometric to get the critical research trends, thematic clusters, and linkages within the field.Using VOSviewer has let us to discover concealed associations, pinpoint the research banks and also to highlight the prominent researchers and institutes, who are promoting collaborations.With VOSviewer’s super powers on the above entities , coupling it with advanced methodologies like AI/ML has made us reshape the research of Sickle Cell Anemia.Utilizing machine learning for disease severity prediction and setting up treatment pipeline.