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The future of hepatology
Scott L Friedman1, Arun J Sanyal2
1Division of Liver Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Hepatology (Baltimore, Md.)
|April 4, 2023
Summary
Hepatology has advanced significantly, yet faces challenges like fatty liver disease and the need for better diagnostics. Future progress requires integrated care, workforce expansion, and embracing data science for liver health.
Area of Science:
- Hepatology
- Gastroenterology
- Internal Medicine
Background:
- The field of hepatology has seen substantial progress over 75 years.
- Key advances include understanding liver function, genetic factors, antiviral therapies, and transplantation.
- Despite progress, challenges remain, including managing fatty liver diseases, autoimmune conditions, cancer, and pediatric liver disease.
Purpose of the Study:
- To review the progress and identify persistent challenges in the field of hepatology.
- To highlight the need for diagnostic advancements and integrated care models.
- To outline future directions for research, training, and clinical practice in hepatology.
Main Methods:
- This is a review and perspective piece, not based on original experimental data.
- It synthesizes current knowledge and expert opinion on the state of hepatology.
- It discusses emerging trends and future needs in liver disease research and patient care.
Main Results:
- Significant advancements have transformed patient outcomes in various liver diseases.
- Emerging challenges include the rising prevalence of fatty liver diseases and complex comorbidities.
- There is an urgent need for improved diagnostic tools for risk stratification and precise testing of new therapies.
Conclusions:
- Hepatology requires innovative solutions for current challenges, including fatty liver disease and complex comorbidities.
- Integrated care models and workforce expansion are essential to manage the growing burden of liver disease.
- Future progress hinges on continued investment in basic and translational research, alongside incorporating data management, AI, and precision medicine into training and practice.
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