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AI enables accurate, reproducible diagnosis while reducing physicians’ workload through automatic or semi-automatic segmentation and registration, and through radiomics that captures quantitative information beyond human perception. Applications span ultrasound, endoscopic ultrasound, CT, MRI, and PET/CT for focal lesions and diffuse diseases, including neoplasms and pancreatitis. The review also addresses clinical workflow steps, such as test selection, image-quality enhancement, faster acquisition, prognosis, and treatment response.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/clinical-impact-of-artificial-intelligence-based-solutions-on-imaging-of-the-pancreas-and-liver/385626/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/clinical-impact-of-artificial-intelligence-based-solutions-on-imaging-of-the-pancreas-and-liver/385626.png","ImageObject",300,407,{"name":92,"@type":93},"Lucas Martin","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-01","2026-09-24",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",13,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"How does AI contribute to imaging interpretation for the pancreas and liver?","Question",{"text":113,"@type":114},"AI supports assisted or automated interpretation of radiological images and can generate accurate, reproducible imaging diagnoses. It reduces physicians’ workload and enables automatic or semi-automatic segmentation and registration.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What additional radiology steps can AI support beyond image interpretation?",{"text":118,"@type":114},"AI can be applied to choose the most convenient test prescription, improve image quality, and accelerate image acquisition. It can also help predict patient prognosis and treatment response.",{"name":120,"@type":111,"acceptedAnswer":121},"Which imaging techniques and disease areas are covered in the review?",{"text":122,"@type":114},"The review describes AI use across ultrasound, endoscopic ultrasonography, CT, MRI, and PET/CT. It includes applications for detection and characterization of focal lesions and diffuse diseases of the liver and pancreas, such as neoplasms and pancreatitis.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},385626,1790626347,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":46,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":31},8796095360427,"https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d","| W | J | G | World Journal of\u003Cbr>Gastroenterology |\n| --- | --- | --- | --- |\n\nSubmit a Manuscript: [https://www.f6publishing.com](https://www.f6publishing.com) World J Gastroenterol 2023 March 7; 29(9): 1427-1445  \nDOI: 10.3748/wjg.v29.i9.1427 ISSN 1007-9327 (print) ISSN 2219-2840 (online)  \nREVIEW  \nClinical impact of artificial intelligence-based solutions on imaging of the pancreas and liver  \nM Alvaro Berbís, Felix Paulano Godino, Javier Royuela del Val, Lidia Alcalá Mata, Antonio Luna  \nSpecialty type: Gastroenterology and hepatology  \nProvenance and peer review:  \nInvited article; Externally peer reviewed.  \nPeer-review model: Single blind  \nPeer-review report’s scientific quality classification  \nGrade A (Excellent): 0  \nGrade B (Very good): B, B  \nGrade C (Good): C  \nGrade D (Fair): 0  \nGrade E (Poor): 0  \nP-Reviewer: Alves N, Portugal; Ma C, China; Xiao B, China  \nReceived: September 28, 2022  \nPeer-review started: September 28, 2022  \nFirst decision: January 3, 2023  \nRevised: January 13, 2023  \nAccepted: February 27, 2023  \nArticle in press: February 27, 2023  \nPublished online: March 7, 2023  \nM Alvaro Berbís, Javier Royuela del Val, Department of Radiology, HT Médica, San Juan de Dios Hospital, Córdoba 14960, Spain  \nM Alvaro Berbís, Faculty of Medicine, Autonomous University of Madrid, Madrid 28049, Spain  \nFelix Paulano Godino, Lidia Alcalá Mata, Antonio Luna, Department of Radiology, HT Médica, Clínica las Nieves, Jaén 23007, Spain  \nCorresponding author: Antonio Luna, MD, PhD, Director, Department of Radiology, HTMédica, Clínica las Nieves, MRI Unit, 2 Carmelo Torres, Jaén 23007, Spain.  \n[aluna70@htmedica.com](aluna70@htmedica.com)  \nAbstract  \nArtificial intelligence (AI) has experienced substantial progress over the last ten years in many fields of application, including healthcare. In hepatology and pancreatology, major attention to date has been paid to its application to the assisted or even automated interpretation of radiological images, where AI can generate accurate and reproducible imaging diagnosis, reducing the physicians’workload. AI can provide automatic or semi-automatic segmentation and registration of the liver and pancreatic glands and lesions. Furthermore, using radiomics, AI can introduce new quantitative information which is not visible to the human eye to radiological reports. AI has been applied in the detection and characterization of focal lesions and diffuse diseases of the liver and pancreas, such as neoplasms, chronic hepatic disease, or acute or chronic pancreatitis, among others. These solutions have been applied to different imaging techniques commonly used to diagnose liver and pancreatic diseases, such as ultrasound, endoscopic ultrasonography, computerized tomography (CT), magnetic resonance imaging, and positron emission tomography/CT. However, AI is also applied in this context to many other relevant steps involved in a comprehensive clinical scenario to manage a gastroenterological patient. AI can also be applied to choose the most convenient test prescription, to improve image quality or accelerate its acquisition, and to predict patient prognosis and treatment response. In this review, we summarize the current evidence on the application of AI to hepatic and pancreatic radiology, not only in regard to the interpretation of images, but also to all the steps involved in the radiological workflow in a broader sense. Lastly, we discuss the challenges and future directions of the clinical application of AI methods.  \n WJG  [https://www.wjgnet.com](https://www.wjgnet.com) 1427 March 7, 2023  Volume 29  Issue 9   \nBerbís MA et al. AI impact on hepatopancreatic imaging  \nKey Words: Artificial intelligence; Machine learning; Deep learning; Imaging; Liver; Pancreas  \n©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.  \nCore Tip: The gastroenterology field is changing with the application of artificial intelligence (AI) solutions capable of as","cbCaio5JKT8QuiiT","https://ap.wps.com/l/cbCaio5JKT8QuiiT","pdf",3690418,"English","# Introduction\n## Clinical workflow roles of AI in hepatic and pancreatic radiology\n## Radiomics and quantitative imaging information\n## Imaging modalities and clinical use cases\n## Future challenges and directions","[{\"question\":\"How does AI contribute to imaging interpretation for the pancreas and liver?\",\"answer\":\"AI supports assisted or automated interpretation of radiological images and can generate accurate, reproducible imaging diagnoses. It reduces physicians’ workload and enables automatic or semi-automatic segmentation and registration.\"},{\"question\":\"What additional radiology steps can AI support beyond image interpretation?\",\"answer\":\"AI can be applied to choose the most convenient test prescription, improve image quality, and accelerate image acquisition. It can also help predict patient prognosis and treatment response.\"},{\"question\":\"Which imaging techniques and disease areas are covered in the review?\",\"answer\":\"The review describes AI use across ultrasound, endoscopic ultrasonography, CT, MRI, and PET/CT. It includes applications for detection and characterization of focal lesions and diffuse diseases of the liver and pancreas, such as neoplasms and pancreatitis.\"}]","Clinical impact of artificial intelligence-based solutions on imaging of the pancreas and liver | PDF",1790270308]