Cooper University Hospital is a teaching hospital and biomedical research facility located in Camden, New Jersey. The hospital formerly served as a clinical campus of Robert Wood Johnson Medical School and the University of Medicine and Dentistry of New Jersey. Affiliated with Cooper Medical School of Rowan University, the hospital offers training programs for medical students, residents, fellows, nurses, and allied health professionals. In partnership with the University of Texas MD Anderson Cancer Center, Cooper operates a comprehensive cancer center serving patients in New Jersey and the Delaware Valley.Cooper is affiliated with the Coriell Institute for Medical Research and is a tertiary partner for twenty-one regional hospitals.
The multidisciplinary heart team (HT) remains the cornerstone of decision-making for complex cardiovascular disease. Large language models (LLMs) and other generative artificial intelligence models have recently emerged as potential decision support tools across diverse clinical settings. We sought to synthesize current evidence and quantitatively estimate concordance between LLM recommendations and HT decisions. A literature search was performed using PubMed, Scopus, and Web of Science for primary studies published between November 2022 and February 2026 that evaluated recommendations by LLMs against multidisciplinary HT decisions. Studies reporting overall agreement were included for quantitative pooling. Random-effects meta-analysis was performed to determine proportion of agreement. Four retrospective concordance studies were included regarding decision-making in coronary revascularization and aortic valve intervention. LLM–HT concordance ranged from 65
The purpose of this study was to compare perioperative outcomes between dialysis-dependent and non-dialysis patients undergoing multilevel lumbar fusion. The National Inpatient Sample (2016–2022) was queried for adults undergoing elective multilevel lumbar fusion. Dialysis dependence was identified using ICD-10-CM codes. Demographic, clinical, and hospital characteristics were compared between groups using survey-weighted analyses. Multivariable logistic regression examined the independent association between dialysis status and complications, non-routine discharge, and inpatient mortality. Significance was set at the P < 0.05 level. Among 459,360 weighted admissions, 840 (0.18
Although the Woven EndoBridge (WEB) device is increasingly used for the treatment of wide-neck intracranial aneurysms, including in the acute rupture setting, comparative evidence assessing the impact of rupture status remains limited. This study compared angiographic, safety, and clinical outcomes between ruptured and unruptured intracranial aneurysms treated with WEB. We conducted a retrospective analysis of prospectively collected data from the multicenter cohort registry WorldWideWEB, including consecutive adult patients with intracranial aneurysms treated with the WEB. Patients were stratified into groups of ruptured and unruptured aneurysms. Propensity score matching was used to balance baseline characteristics between both groups. Retreatment rate was the primary outcome. Secondary outcomes included mRS, safety events (thromboembolic complications) and angiographic outcomes (periprocedurally and last follow-up). Among 1,220 patients, 342 (28.0
Adult intestinal malrotation is rare, and data comparing minimally invasive surgery (MIS) to open Ladd’s procedures in this population are limited. This study evaluates perioperative outcomes and MIS utilization in adults. A retrospective cohort study using the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) 2022–2023 datasets identified adults undergoing Ladd’s procedure. The primary outcome was total hospital length of stay (tLOS); secondary outcomes included postoperative LOS (pLOS), 30-day morbidity, and mortality. Multivariable linear and logistic regression identified independent predictors of LOS and MIS utilization. Of 142 Ladd’s procedures identified in adult patients (109 open, 33 MIS), MIS patients were younger (mean age 47.0 vs. 58.2 years, p = 0.005), more often female (78.8
Millions of patients are regularly using large language model (LLM) chatbots for medical advice, raising patient safety concerns. This physician-led red-teaming study compares the safety of four publicly available chatbots—Claude by Anthropic, Gemini by Google, GPT-4o by OpenAI, and Llama-3.0/3.1-70B by Meta—on a new dataset, HealthAdvice, using an evaluation framework that enables quantitative and qualitative analysis. In total, 888 chatbot responses are evaluated for 222 patient-posed advice-seeking medical questions on primary care topics spanning internal medicine, women’s health, and pediatrics. We find statistically significant differences between chatbots. The rate of problematic responses varies from 21.6% (Claude) to 43.2% (Llama), with unsafe responses varying from 5% (Claude) to 13% (GPT-4o, Llama). Qualitative results reveal chatbot responses with the potential to lead to serious patient harm. This study suggests that millions of patients could be receiving unsafe medical advice from publicly available chatbots, and further work is needed to improve the clinical safety of these powerful tools.
