Please use this identifier to cite or link to this item:
doi:10.22028/D291-44627
Title: | Revolutionizing MASLD: How Artificial Intelligence Is Shaping the Future of Liver Care |
Author(s): | Pugliese, Nicola Bertazzoni, Arianna Hassan, Cesare Schattenberg, Jörn M. Aghemo, Alessio |
Language: | English |
Title: | Cancers |
Volume: | 17 |
Issue: | 5 |
Publisher/Platform: | MDPI |
Year of Publication: | 2025 |
Free key words: | fatty liver disease liver steatosis deep machine learning chatbot metabolic syndrome |
DDC notations: | 610 Medicine and health |
Publikation type: | Journal Article |
Abstract: | Metabolic dysfunction-associated steatotic liver disease (MASLD) is emerging as a leading cause of chronic liver disease. In recent years, artificial intelligence (AI) has attracted significant attention in healthcare, particularly in diagnostics, patient management, and drug development, demonstrating immense potential for application and implementation. In the field of MASLD, substantial research has explored the application of AI in various areas, including patient counseling, improved patient stratification, enhanced diagnostic accuracy, drug development, and prognosis prediction. However, the integration of AI in hepatology is not without challenges. Key issues include data management and privacy, algorithmic bias, and the risk of AI-generated inaccuracies, commonly referred to as “hallucinations”. This review aims to provide a comprehensive overview of the applications of AI in hepatology, with a focus on MASLD, highlighting both its transformative potential and its inherent limitations. |
DOI of the first publication: | 10.3390/cancers17050722 |
URL of the first publication: | https://doi.org/10.3390/cancers17050722 |
Link to this record: | urn:nbn:de:bsz:291--ds-446275 hdl:20.500.11880/39778 http://dx.doi.org/10.22028/D291-44627 |
ISSN: | 2072-6694 |
Date of registration: | 12-Mar-2025 |
Faculty: | M - Medizinische Fakultät |
Department: | M - Innere Medizin |
Professorship: | M - Keiner Professur zugeordnet |
Collections: | SciDok - Der Wissenschaftsserver der Universität des Saarlandes |
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cancers-17-00722-v2.pdf | 734,92 kB | Adobe PDF | View/Open |
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