Kathryn Thompson

Chronic hepatitis C virus infection (HCV) is one of the leading causes of hepatocellular carcinoma (HCC) in the United States. Using a retrospective cross‐sectional analysis, this study identified patient characteristics and factors associated with the development of HCV‐associated HCC in 113 patients seen in the primary care‐based HCV clinic in Grady Memorial Hospital, a public, safety‐net healthcare facility in Atlanta, Georgia from October 2013 to November 2024. The study population was comprised of mostly African American men with high rates of polysubstance use, 96.5% of whom had cirrhosis. Patients treated for HCV prior to HCC diagnosis were older at HCC diagnosis (mean difference 3.248, p ‐value 0.037), had a longer latency period between HCV and HCC diagnoses (mean difference 38.740, p ‐value 0.002), had prior HCC screening (mean difference 0.547, p ‐value 0.000), and less time from last HCC screening to HCC diagnosis (mean difference −25.332, p ‐value 0.012), and had a lower Child‐Pugh Score at time of HCC diagnosis (mean difference −0.300, p ‐value 0.036) compared to patients who were not treated for HCV. For patients who achieved SVR prior to HCC, HCV genotype 1 was associated with longer time to HCC diagnosis ( p ‐value 0.030), and a higher FIB‐4 index at the time of achieving SVR and longer knowledge of HCV infection were associated with shorter time to developing HCC ( p ‐value 0.003; p ‐value 0.044). Ultimately, this study highlights the importance of understanding risk factors for HCV‐associated HCC in underserved populations which could help providers tailor their approach to mitigate the risk of HCC.

The integration of artificial intelligence (AI) into hepatology is revolutionizing the diagnosis and management of liver diseases amidst a rising global burden of conditions like metabolic-associated steatotic liver disease (MASLD). AI harnesses vast datasets and complex algorithms to enhance clinical decision making and patient outcomes. AI’s applications in hepatology span a variety of conditions, including autoimmune hepatitis, primary biliary cholangitis, primary sclerosing cholangitis, MASLD, hepatitis B, and hepatocellular carcinoma. It enables early detection, predicts disease progression, and supports more precise treatment strategies. Despite its transformative potential, challenges remain, including data integration, algorithm transparency, and computational demands. This review examines the current state of AI in hepatology, exploring its applications, limitations, and the opportunities it presents to enhance liver health and care delivery.

The integration of artificial intelligence (AI) into hepatology is revolutionizing the diagnosis and management of liver diseases amidst a rising global burden of conditions like metabolic-associated steatotic liver disease (MASLD). AI, particularly machine learning (ML) and deep learning (DL), harnesses vast datasets and complex algorithms to enhance clinical decision-making and patient outcomes. AI's applications in hepatology span a variety of conditions, including autoimmune hepatitis, primary biliary cholangitis, primary sclerosing cholangitis, MASLD, hepatitis B, and hepatocellular carcinoma. It enables early detection, predicts disease progression, such as fibrosis and cirrhosis, and supports more precise treatment strategies. Despite its transformative potential, challenges remain, including data integration, algorithm transparency, and computational demands. This review examines the current state of AI in hepatology, exploring its applications, limitations, and the opportunities it presents to enhance liver health and care delivery.

Purpose: Helping medical students maintain wellbeing has become an important concern, as many medical students report a decline in their mental health during the course of their training. To improve students’ wellbeing, some schools have implemented wellness programs into their curricula. While there is growing research about the effectiveness of these programs, little is known about what medical students themselves desire to support their wellbeing. This study aimed to assess medical student perspectives regarding the most effective ways to promote wellness during medical school. Participants and Methods: To address this gap in the literature, a survey was administered to medical students at the nine medical schools in the state of Florida. Participants included 864 medical students, whose anonymous responses were analyzed descriptively as well as qualitatively to determine major themes. Results: Students provided novel suggestions and recommendations, including ideas for curricular additions and changes, cultural changes within schools, promoting positive behaviors and extracurricular activities, and providing resources for students. Conclusion: Based on the participants’ responses, it is evident that wellness is an important issue to medical students and, given the variety of suggestions, schools should strongly consider what wellness changes to implement and whether participation in them should be mandatory. The results of this study will be a resource to medical schools and educators who are considering curricular changes to address medical student wellbeing.