Mapping the global landscape of artificial intelligence in pancreatic cancer research
TL;DR
Artificial intelligence research in pancreatic cancer is growing rapidly and becoming increasingly multidisciplinary, although productivity and collaboration remain uneven.
Abstract
Background: Artificial intelligence (AI) applications in pancreatic cancer are expanding rapidly, but the field’s global structure and emerging priorities remain incompletely characterized. Methods: The 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. Results: Publication 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. Conclusion: AI 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.