To comprehensively analyze the current research landscape of artificial intelligence (AI) in lymphoma, identify future developments, and provide references for clinicians and researchers, focusing on diagnostic and therapeutic applications.
Key Findings:
A total of 662 publications were identified, indicating a growing trend in AI research related to lymphoma.
The analysis revealed key authors, institutions, and journals contributing to the field, highlighting their impact on advancing research.
Emerging research topics include the application of machine learning and deep learning in lymphoma diagnosis and treatment, which may lead to improved patient outcomes.
Interpretation:
The bibliometric analysis highlights the increasing integration of AI technologies in lymphoma research, suggesting a shift towards more precise and efficient diagnostic and therapeutic strategies, ultimately enhancing patient care.
Limitations:
The study is limited to publications in English, potentially excluding relevant research in other languages, which may affect the comprehensiveness of the findings.
The analysis is confined to the WOSCC database, which may not encompass all relevant literature, possibly leading to gaps in the research landscape.
Conclusion:
The study provides a foundational understanding of the role of AI in lymphoma research, indicating significant potential for future advancements in diagnosis and treatment.
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