ArticleDigital health
Research hotspots and trends of artificial intelligence in lymphoma: A bibliometric analysis from 2010 to 2024.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: At present, artificial intelligence (AI) plays a significant role in the diagnosis, treatment, and prognosis of lymphoma. This study quantitatively analyzed the research hotspots and future trends in this field by using bibliometric software. The aim is to offer researchers a full understanding of the current research along with suggestions for future directions. Methods: All relevant articles of AI in lymphoma research were retrieved from the Web of Science Core Collection database from 2010 to 2024. Bibliometric visualization analysis of all retrieved data was conducted by using the " Results: Analysis of 662 publications shows that AI in lymphoma research is currently on rapid development. The United States and China are ahead of other countries in terms of the number of articles and citations. Jiang Huiyan from Northeastern University (China) and Michel Meignan from Assistance Publique-Hôpitaux de Paris (France) are the most prolific and highly cited authors in this field. Recent hotspots focus on molecular expression and imaging histology, while the emerging directions are molecular mechanisms and chemotherapeutic strategies in lymphoma. Conclusions: This study comprehensively analyzes the hotspots and trends of AI research in lymphoma, which is shifting from radiomics toward molecular mechanisms and AI-optimized chemotherapy. In the future, it is necessary to meet the practical clinical demands and promote the integration of AI.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.