Artificial Intelligence in Abdominal MRI Diagnostics: Current Applications, Challenges, and Future Perspectives

Abstract

The increasing availability of large image data sets and technical advances in the field of information technology have also greatly advanced the use of artificial intelligence (AI) in radiology in recent years. Especially in the field of abdominal MRI diagnostics, there are numerous opportunities to use AI applications to provide efficient, objective, and standardized image acquisition and diagnosis.This review summarizes the current state of research and clinical application of AI in abdominal MRI diagnostics with the help of a literature search via PubMed. The focus is on interpretive areas of application such as automatic segmentation of abdominal organs, classification of pathologies, and quantitative analysis of a wide range of abdominal diseases. In addition, the technical requirements, challenges and limitations as well as ethical aspects are systematically examined.AI-based systems show promising preclinical results, for example, in image reconstruction, segmentation, detection and characterization of lesions, as well as in the classification, for example, of PSC-typical bile duct changes based on MRCP. Interestingly, however, compared to other organ-specific applications in radiology, there are only a few clinically usable tools in abdominal imaging. In addition, there are still major challenges due to the often very heterogeneous data quality, the availability of carefully annotated image data, and legal and ethical safeguards. However, the issues of cost structure and profitability, as well as the remuneration of AI-based applications, also play a significant role and need to be clarified.Despite the great potential and promising preclinical work, the integration of AI systems in abdominal MRI is not yet established in everyday clinical practice. Successful clinical implementation requires standardized workflows, transparent model architecture, legally compliant framework conditions, clear reimbursement guidelines, and the active involvement of radiological expertise. In the future, multimodal, predictive systems with the integration of supplementary clinical data and the ethically reflected design of AI-supported decision-making processes will become increasingly important. · Compared to other application areas within radiology, there are still very few dedicated and validated AI applications for abdominal MRI, which is mainly due to the comparatively complex data structure and the high inter-individual variability of the abdomen.. · For successful integration into clinical practice, it is essential to have multi-center training data sets, such as those found in the context of large cohort studies, as well as transparent data protection and competitive remuneration.. · Ragab H, Aydemir DG, Cicek H et al. Artificial Intelligence in Abdominal MRI Diagnostics: Current Applications, Challenges, and Future Perspectives. Rofo 2025; DOI 10.1055/a-2704-7577.

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