ArticleMedicine2026
Mapping the global landscape of artificial intelligence in pancreatic cancer research: A bibliometric and visualization analysis.
Article in Medicine, 2026. 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.
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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
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) applications in pancreatic cancer are expanding rapidly, but the field's global structure and emerging priorities remain incompletely characterized.
methodsThe Web of Science Core Collection was searched through July 5, 2026. English-language original articles and reviews addressing AI in pancreatic cancer were eligible. After manual screening, 583 publications from 1998 to 2026 were analyzed using Biblioshiny, VOSviewer, and CiteSpace to assess publication trends, contributors, collaboration networks, co-citation patterns, keyword evolution, thematic clusters, and citation bursts.
resultsPublication output accelerated after 2019 and reached 164 publications in 2025; the lower total in 2026 reflected partial-year coverage. The literature involved 59 countries, 1230 institutions, and 4038 authors. China produced the largest number of publications (n = 222), whereas the United States ranked second (n = 150) and had the highest country-level centrality (0.39). Shanghai Jiao Tong University was the most productive institution (n = 20), while Harvard University had the highest institutional centrality (0.22). Frontiers in Oncology was the most productive journal (n = 32). Influential studies focused on deep learning-based computed tomography, electronic health record-based risk prediction, exosome-based machine learning, and endoscopic ultrasonography. Research themes evolved from neural-network classification and texture analysis toward radiomics, deep learning, early detection, risk prediction, liquid biopsy, tumor biology, treatment-response prediction, and precision oncology.
conclusionAI research in pancreatic cancer is growing rapidly and becoming increasingly multidisciplinary, although productivity and collaboration remain uneven. Clinical translation will require prospective multicenter validation, representative datasets, transparent reporting, calibration, fairness assessment, workflow evaluation, and evidence of improved patient outcomes.
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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.