AI-powered innovations in pancreatitis imaging: a comprehensive literature synthesis

• Published in: Abdominal radiology (New York)
• Main Authors: Maletz, Sebastian, Balagurunathan, Yoga, Murphy, Kade, Folio, Les, Chima, Ranjit, Zaheer, Atif, Vadvala, Harshna
• Format: Journal Article
• Language: English
• Published: 12.08.2024
• ISSN: 2366-0058; 2366-0058
• DOI: 10.1007/s00261-024-04512-4

Early identification of pancreatitis remains a significant clinical diagnostic challenge that impacts patient outcomes. The evolution of quantitative imaging followed by deep learning models has shown great promise in the non-invasive diagnosis of pancreatitis and its complications. We provide an overview of advancements in diagnostic imaging and quantitative imaging methods along with the evolution of artificial intelligence (AI). In this article, we review the current and future states of methodology and limitations of AI in improving clinical support in the context of early detection and management of pancreatitis.Early identification of pancreatitis remains a significant clinical diagnostic challenge that impacts patient outcomes. The evolution of quantitative imaging followed by deep learning models has shown great promise in the non-invasive diagnosis of pancreatitis and its complications. We provide an overview of advancements in diagnostic imaging and quantitative imaging methods along with the evolution of artificial intelligence (AI). In this article, we review the current and future states of methodology and limitations of AI in improving clinical support in the context of early detection and management of pancreatitis.