Applications of artificial intelligence in abdominal imaging.
The rapid advancements in artificial intelligence (AI) carry the promise to reshape abdominal imaging by offering transformative solutions to challenges in disease detection, classification, and personalized care. AI applications, particularly those leveraging deep learning and radiomics, have demon...
| Publicado en: | Abdominal Radiology Vol. 50; no. 12; pp. 6172 - 6192 |
|---|---|
| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Dec2025
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=189211978&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 189211978 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Dec2025 vid: 50 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 189211978 185412301 10.1007/s00261-025-04990-0 189211978 ppf: 6172 ppct: 20 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Applications of artificial intelligence in abdominal imaging. aug: au: Gupta, Amit Rajamohan, Naveen Bansal, Bhavik Chaudhri, Sukriti Chandarana, Hersh Bagga, Barun affil: https://ror.org/02dwcqs71 All India Institute of Medical Sciences, New Delhi, India sug: ab: The rapid advancements in artificial intelligence (AI) carry the promise to reshape abdominal imaging by offering transformative solutions to challenges in disease detection, classification, and personalized care. AI applications, particularly those leveraging deep learning and radiomics, have demonstrated remarkable accuracy in detecting a wide range of abdominal conditions, including but not limited to diffuse liver parenchymal disease, focal liver lesions, pancreatic ductal adenocarcinoma (PDAC), renal tumors, and bowel pathologies. These models excel in the automation of tasks such as segmentation, classification, and prognostication across modalities like ultrasound, CT, and MRI, often surpassing traditional diagnostic methods. Despite these advancements, widespread adoption remains limited by challenges such as data heterogeneity, lack of multicenter validation, reliance on retrospective single-center studies, and the "black box" nature of many AI models, which hinder interpretability and clinician trust. The absence of standardized imaging protocols and reference gold standards further complicates integration into clinical workflows. To address these barriers, future directions emphasize collaborative multi-center efforts to generate diverse, standardized datasets, integration of explainable AI frameworks to existing picture archiving and communication systems, and the development of automated, end-to-end pipelines capable of processing multi-source data. Targeted clinical applications, such as early detection of PDAC, improved segmentation of renal tumors, and improved risk stratification in liver diseases, show potential to refine diagnostic accuracy and therapeutic planning. Ethical considerations, such as data privacy, regulatory compliance, and interdisciplinary collaboration, are essential for successful translation into clinical practice. AI's transformative potential in abdominal imaging lies not only in complementing radiologists but also in fostering precision medicine by enabling faster, more accurate, and patient-centered care. Overcoming current limitations through innovation and collaboration will be pivotal in realizing AI's full potential to improve patient outcomes and redefine the landscape of abdominal radiology. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|