ArticleAnnals of medicine and surgery (2012)2025
Artificial Intelligence in IPMN diagnosis: bridging promise and clinical reality.
Article in Annals of medicine and surgery (2012), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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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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Authors and funding
5 authors.
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Abstract
Intraductal papillary mucinous neoplasms (IPMNs) are pancreatic cystic tumors with malignant potential, requiring accurate risk stratification for management. Current diagnostic reliance on imaging and clinical guidelines is limited by subjectivity and moderate sensitivity. Artificial intelligence (AI), particularly machine learning and deep learning models, has demonstrated significant promise in distinguishing benign from high-risk lesions using CT, MRI, and endoscopic ultrasound. Studies have reported diagnostic accuracies as high as 94-99.6%, outperforming traditional clinical assessments. Despite these advances, widespread implementation is hindered by lack of standardized imaging protocols, methodological transparency, and prospective multicenter validation. Integrating AI into clinical practice and international guidelines may enhance precision diagnostics and improve prognostic accuracy in IPMN management.
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