Objective:

The application of artificial intelligence (AI) in leukemia management is rapidly expanding, with components such as machine learning (ML), deep learning (DL), and neural networks (NNs) offering innovative solutions for diagnosis and treatment. This study aims to analyze the global impact of AI in leukemia research through bibliometric analysis, highlighting trends in scientific production, institutional contributions, and keyword evolution.

Materials and Methods:

A systematic literature review was conducted using Scopus and Web of Science (WoS) databases. Inclusion criteria focused on AI applications in leukemia, incorporating research articles and conference papers while excluding reviews and non-related studies. Data were analyzed using VosViewer (Version 1.6.20) and Bibliometrix-Biblioshiny to map publication trends, co-authorship networks, and keyword co-occurrence.

Results:

A total of 248 documents from Scopus and 472 from WoS were analyzed. Machine learning emerged as the most frequently studied AI tool, followed by NNs and DL. A significant increase in AI-related leukemia research has been observed since 2017. The United States and China were the most active contributors. Studies primarily focused on acute leukemia, while chronic leukemia subtypes received comparatively less attention. Institutions and journals have increasingly prioritized AI in leukemia research, indicating growing academic and clinical interest.

Conclusion:

The integration of AI into leukemia research is accelerating, with ML leading the way. However, more studies are needed to explore chronic leukemia subtypes and translate AI-driven advancements into clinical practice. The increasing global interest in AI applications suggests that these technologies will play a crucial role in future leukemia management.

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