Nader Dbouk

Purpose of Review Currently, there are number of initiatives underway across the world to remove barriers and increase education about living donation and living donor transplantation, including among professionals. Widespread use of the internet and social media (SoMe) started a new era of online information sharing. Extensive studies have highlighted the significant role of visual abstracts (VAs) and infographics in disseminating medical information including related to donation and transplantation. SoMe can foster networking and collaboration between transplant professionals across the globe. Recent Findings The American Society of Transplantation Living Donor Community of Practice (LDCOP) Multimedia Workgroup (MMWG) was established to support the LDCOP’s professional education mission by providing audiovisual support for various initiatives using visual abstracts, infographics, and professional community engagement. The MMWG employs rigorous multi-layered vetting by professionals with academic expertise to ensure the reliability and accuracy of the content, pioneering a model for creating visual educational content, collaborative learning and skill-building across experience levels. Summary In this perspective review, we summarize the history of a professional society multimedia work group and our experience building a multimedia approach to living donation education for transplant professionals.

Hepatocellular carcinoma (HCC), the most common primary liver cancer, remains a leading cause of cancer-related mortality worldwide. Its often-silent progression results in late-stage diagnosis, limiting curative options and necessitating systemic therapy for many patients. The presence of underlying cirrhosis in most cases further complicates treatment decisions. While the approval of sorafenib in 2007 marked a major milestone in systemic therapy for HCC, the treatment landscape has since evolved significantly, particularly with the advent of immune checkpoint inhibitors and anti-angiogenic agents. Combination regimens, such as atezolizumab plus bevacizumab, have demonstrated superior outcomes and are now considered standard first-line options. Despite these advances, efforts to translate insights from HCC’s molecular pathogenesis into personalized treatments have been limited. This narrative review explores the current systemic therapy options for HCC, from first-line to subsequent-line treatments, and highlights emerging strategies, including novel immunotherapies and targeted agents. We emphasize the need for individualized treatment approaches that consider tumor biology, liver function, and performance status, and we outline future directions for research aimed at improving outcomes in this complex and evolving field.

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.

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Background and Aims:  Disease recurrence following transplantation occurs in 20–45% of patients with autoimmune hepatitis (AIH). Factors associated with an increased risk of recurrence include human leukocyte antigen (HLA) DR3 and HLA DR4 positivity, inadequate immunosuppression, and severity of inflammation in the native liver. Titers of several autoantibodies can be elevated in patients with AIH, including antinuclear antibody (ANA) and antismooth muscle antibody (SMA); however, it is unclear whether or not the degree of elevation influences the risk of disease recurrence following transplantation.Methods:  We conducted a retrospective study to evaluate the potential impact of pretransplant titers on post‐transplant outcomes for patients with AIH. Sixty‐three patients with AIH who underwent 72 liver transplants between 1 January 1989 and 1 January 2009 were included, with a median follow up of 10 months. Patients were divided into group A (ANA or SMA ≥ 1 : 160) and group B (titers ≤ 1 : 160).Results:  There was no significant difference in the recurrence rates or death between patients in groups A and B, respectively. Only race appeared to impact outcomes, with African American patients having a higher incidence of death and recurrent disease post‐transplant compared to other ethnicities.Conclusions:  Based on our findings, pretransplant ANA and SMA levels do not appear to impact recurrence rates or outcomes following liver transplantation for AIH.