To explore the role of chronic liver disease (CLD) in COVID-19. A total of 1439 consecutively hospitalized patients with COVID-19 from one large medical center in the United States from March 16, 2020 to April 23, 2020 were retrospectively identified. Clinical characteristics and outcomes were compared between patients with and without CLD. Postmortem examination of liver in 8 critically ill COVID-19 patients was performed. There was no significant difference in the incidence of CLD between critical and non-critical groups (4.1% vs 2.9%, p = 0.259), or COVID-19 related liver injury between patients with and without CLD (65.7% vs 49.7%, p = 0.065). Postmortem examination of liver demonstrated mild liver injury associated central vein outflow obstruction and minimal to moderate portal lymphocytic infiltrate without evidence of CLD. Patients with CLD were not associated with a higher risk of liver injury or critical/fatal outcomes. CLD was not a significant comorbid condition for COVID-19.

Physicians frequently confront time-sensitive decisions under uncertain conditions, necessitating reliable tools for forecasting clinical outcomes. Although clinical predictive models have the potential to assist in these critical decisions, their widespread adoption is hindered by complexities in data handling, model development, and integration into clinical workflows. This study introduces a novel framework (Hopkins LLM) leveraging structured electronic health records (EHRs) data to develop and deploy clinical large language models (LLMs) that act as multi-task-capable predictive engines to support clinically constrained decision-support tasks with minimal barriers to implementation. Employing the advanced LLaMA architecture, consisting of 7 billion parameters, our model was pre-trained on a comprehensive corpus and subsequently fine-tuned and tested on a dataset of 42,160 patients within Johns Hopkins Health System, addressing a spectrum of clinical and operational prediction tasks. We validated our model across three diverse external health systems and four key prediction tasks involving 1,329 patients, including 30-day all-cause readmissions, 90-day all-cause mortality, 30-day intensive care unit (ICU) admissions, and treatment recommendations. The proposed Hopkins-LLM framework achieved a mean area under the receiver operating characteristic curve (ROC-AUC) of 0.84 [0.82, 0.88], yielding a significant 0.28 advancement over zero-shot baseline LLMs (p<0.05). These findings underscore the promise of LLMs as unified, user-friendly clinical prediction systems, adept at reasoning across diverse data sources to enhance decision-making at the point of care.

Ultrasound is a cornerstone of emergency and hepatobiliary imaging, yet its interpretation remains highly operator-dependent and time-sensitive. Here, we present a multitask vision-language agent (VLM) developed to assist with right upper quadrant (RUQ) ultrasound interpretation across the diagnostic workflow. The system was trained on data from a single tertiary academic medical center (9189 cases, 594,099 images) and evaluated on datasets from two independent institutions (1704 cases, 90,616 images; and 108 cases, 3240 images). The agent integrates frame-level visual understanding with report-grounded language reasoning to perform three tasks: (i) classification of 16 clinical findings, (ii) generation of diagnostic reports, and (iii) surgical decision support based on ultrasound findings and clinical data. For classification, the model achieved macro areas under the receiver operating characteristic curve (AUROCs) of 0.820, 0.794, and 0.775 on the internal, University of Colorado, and Stanford cohorts, respectively, and an internal micro AUROC of 0.865. It also generated post-edited reports that were difficult for radiologists to distinguish from expert-written versions in blinded evaluations. The agent further predicted cholecystectomy using generated reports and clinical data. These results support further evaluation of vision-language models for improving diagnostic consistency, reporting efficiency, and surgical triage in ultrasound practice.