Jacobi Medical Center (NYC Health + Hospitals/Jacobi) is a municipal hospital operated by NYC Health + Hospitals in affiliation with the Albert Einstein College of Medicine. The facility is located in the Morris Park neighborhood of the Bronx, New York City. It is named in honor of German physician Abraham Jacobi, who is regarded as the father of American pediatrics.Founded in 1955 as Bronx Municipal Hospital Center, the hospital opened concurrent with the opening of the Albert Einstein College of Medicine. This was the first time a medical school and municipal hospital entered into a formal affiliation agreement at the same time they were both built—and their relationship continues to this day. Jacobi is a primary clerkship site for 3rd- and 4th-year medical students from the Albert Einstein College of Medicine. Jacobi offers residency training programs in Internal Medicine, Pediatrics and Radiology. It also offers many joint residency programs with Montefiore Medical Center.Jacobi provides health care for some 1.2 million Bronx and New York City area residents. It is one of the 11 acute care hospitals of NYC Health + Hospitals and a partner in the North Bronx Healthcare Network with the North Central Bronx Hospital. As one of the largest medical facilities of NYC, Jacobi houses the Bronx's only burn unit and Level I trauma center. The hospital also houses a Level III neonatal intensive care unit and FDNY EMS Station 20 (formerly NYC*EMS Station 23). Jacobi had over 320,000 clinical visits and over 100,000 emergency department visits in 2016. Jacobi also houses the only Snakebite Treatment Center in the tri-state area.The Jacobi Hyperbaric Medicine Service provides care for both inpatient and ambulatory patients in a large facility featuring a spacious, 23-foot long chamber. This room-like chamber is one which patients and medical support staff can actually walk into. It can comfortably accommodate up to nine patients at a time. Operating around the clock, the Hyperbaric Medicine Service provides life-saving care for burn patients and smoke inhalation injuries. In addition, Jacobi is the designated New York City Referral Center for the Diver's Alert Network, providing emergency care for decompression sickness. It also treats patients with gas gangrene and acute air embolism, and is widely used on an elective basis to promote wound healing from radiation injuries, compromised skin grafts, diabetic ulcers, and osteomyelitis.
Clinical diagnosis is a complex reasoning process in which clinicians gather evidence, form hypotheses, and test them against alternative explanations. In medical training, this reasoning is explicitly developed through counterfactual questioning–e.g., asking how a diagnosis would change if a key symptom were absent or altered–to strengthen differential diagnosis skills. As large language model (LLM)-based systems are increasingly used for diagnostic support, ensuring the interpretability of their recommendations becomes critical. However, most existing LLM-based diagnostic agents reason over fixed clinical evidence without explicitly testing how individual findings support or weaken competing diagnoses. In this work, we propose a counterfactual multi-agent diagnostic framework inspired by clinician training that makes hypothesis testing explicit and evidence-grounded. Our framework introduces counterfactual case editing to modify clinical findings and evaluate how these changes affect competing diagnoses. We further define the Counterfactual Probability Gap, a method that quantifies how strongly individual findings support a diagnosis by measuring confidence shifts under these edits. These counterfactual signals guide multi-round specialist discussions, enabling agents to challenge unsupported hypotheses, refine differential diagnoses, and produce more interpretable reasoning trajectories. Across three diagnostic benchmarks and seven LLMs, our method consistently improves diagnostic accuracy over prompting and prior multi-agent baselines, with the largest gains observed in complex and ambiguous cases. Human evaluation further indicates that our framework produces more clinically useful, reliable, and coherent reasoning. These results suggest that incorporating counterfactual evidence verification is an important step toward building reliable AI systems for clinical decision support.
Clinical expertise improves not only by acquiring medical knowledge, but by accumulating experience that yields reusable diagnostic patterns. Recent LLMs-based diagnostic agents have shown promising progress in clinical reasoning for decision support. However, most approaches treat cases independently, limiting experience reuse and continual adaptation. We propose SEA, a self-learning diagnostic agent with cognitively inspired dual-memory module. We design a reinforcement training framework tailored to our designed agent for joint optimization of reasoning and memory management. We evaluate SEA in two complementary settings. On standard evaluation with MedCaseReasoning dataset, SEA achieves 92.46% accuracy, outperforming the strongest baseline by +19.6%, demonstrating the benefit of jointly optimizing reasoning and memory. On the long-horizon with ER-Reason dataset, SEA attains the best final accuracy (0.7214) and the largest improvement (+0.35 $\Delta$Acc@100), while baseline methods show limited or unstable gains. Expert evaluation further indicates that rules consolidated from SEA show strong clinical correctness, usefulness and trust, suggesting that the induced rules in dual-memory module are reliable and practically meaningful. Overall, SEA improves both diagnostic reasoning ability and continual learning by effectively transforming experience into reusable knowledge.
Obesity and type 2 diabetes are the primary drivers of atherosclerotic cardiovascular disease (ASCVD), the leading cause of death worldwide. Injectable GLP-1 receptor agonists reduce major adverse cardiovascular events. Yet for many individuals, injection hesitancy remains a significant barrier to long-term adherence. Orforglipron is a novel once-daily oral non-peptide GLP-1 receptor agonist designed to provide comprehensive cardiometabolic risk reduction. PRISMA-compliant systematic review and meta-analysis (PROSPERO CRD420251229397) of placebo-controlled phase 2 and phase 3 trials of orforglipron. Random-effects models were used to pool mean differences (MD) and risk ratios (RR) with 95
Venous congestion is a major contributor to organ dysfunction in critically ill and perioperative patients. While Doppler-based ultrasound strategies such as VExUS are the focus of growing clinical and research interest, the common femoral vein (CFV) is a promising, easily accessible alternative window for assessing right heart function and volume status. To map and synthesize current evidence on the use of common femoral vein (CFV) Doppler ultrasound to assess venous congestion, right heart function, and intravascular volume status in adult patients across perioperative, critical care, heart failure, and emergency care settings. Scoping review conducted according to the PRISMA-ScR guideline. PubMed, Embase, Scopus, and the Cochrane Library were searched from inception to August 2025. We charted clinical setting, CFV Doppler/diameter parameters, acquisition protocol details, reference standards (invasive pressures and imaging-based surrogates), and reported associations with hemodynamic measures and clinical outcomes. Two reviewers independently screened records and extracted data. Nineteen observational studies (n = 2146) were included. CFV pulsatility or waveform morphology was assessed in 10/19 studies; 5/19 reported quantitative pulsatility indices or retrograde-flow thresholds, 5/19 evaluated femoral vein diameter/collapsibility, and 1/19 proposed derived indices. Most studies compared CFV measures with invasive central venous pressure (CVP) or echocardiographic surrogates; when correlation coefficients were reported, associations were weak-to-moderate (e.g., r = 0.66 for CFV diameter vs CVP; r = − 0.476 for minimum velocity vs CVP). Only a minority of studies assessed clinical outcomes, and abnormal CFV patterns were variably associated with postoperative complications, including acute kidney injury, delirium and, in ICU cohorts, longer ICU length of stay or mortality. Acquisition protocols and waveform interpretation criteria varied across studies, with heterogeneous definitions and thresholds. CFV Doppler is a feasible and accessible tool for congestion assessment, with promising correlations to invasive measures. However, variability in acquisition protocols, waveform definitions, and thresholds limits its current applicability. Standardization and prospective validation in high-risk populations are needed.
