AI in Hepatology: Revolutionizing the Diagnosis and Management of Liver Disease
Author Information
Author(s): Malik Sheza, Das Rishi, Thongtan Thanita, Thompson Kathryn, Dbouk Nader
Primary Institution: Rochester General Hospital; Emory University School of Medicine
Hypothesis
The integration of artificial intelligence (AI) into hepatology can enhance the diagnosis and management of liver diseases.
Conclusion
AI has the potential to significantly improve early detection and management strategies for liver diseases, despite existing challenges.
Supporting Evidence
- AI can enhance clinical decision making and patient outcomes in liver disease management.
- Machine learning techniques improve predictive capabilities for liver disease diagnosis.
- AI applications in hepatology include early detection and personalized treatment strategies.
Takeaway
AI helps doctors find and treat liver diseases better and faster, but there are still some problems to solve.
Methodology
This review examines the current state of AI in hepatology, exploring its applications, limitations, and opportunities.
Potential Biases
Many AI models are developed using imbalanced or non-representative datasets, limiting their applicability across diverse populations.
Limitations
Challenges include data integration, algorithm transparency, and computational demands.
Digital Object Identifier (DOI)
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