Latest AI and machine learning research in hospital-based medicine for healthcare professionals.
Patient satisfaction feedback is crucial for hospital service quality, but manual reviews are not possible due to their time-consumption, and traditional natural language processing methods remain inadequate. Large Language Models (LLMs) show promise but are prone to logical hallucinations—fabricated or illogical outputs that limit their reliability (inconsistent performance across repeated uses) ...
Hospitals are increasingly adopting artificial intelligence (AI) tools in clinical care. However, their overall impact on the health of older adults remains unclear. We assessed whether county-level hospital AI implementation was associated with elder mortality and care quality using national data from the American Hospital Association (AHA), Centers for Medicare & Medicaid Services (CMS), and CDC...
Cardiovascular medicine is rapidly evolving, as it integrates digital technologies intended to decentralize care from the clinic and/or hospital setti...
Machine learning models support many clinical tasks; however, challenges arise with the transportability of these models across a network of healthcar...
Predicting hospital readmission in cancer patients-particularly those with metastatic disease-remains a significant clinical challenge. While metastas...
Although hydrogen peroxide (H2O2) nebulization has shown promise for reducing SARS-CoV-2 loads in healthcare settings, its precise kinetics and real-w...
Phage predation is inversely associated with severe cholera yet its importance as a determinant of dehydration severity is unknown relative to other f...
Electroconvulsive therapy (ECT) is an effective treatment of severe manifestations of mental illness. Since delay in initiation of ECT can have detrim...
As the use of AI in healthcare is rapidly expanding, there is also growing recognition of the need for ongoing monitoring of AI after implementation, ...
Organ fibrosis caused by the presence of excessive extracellular matrix (ECM) is strongly related to mortality. Urinary peptide signatures were report...
Trauma remains a leading cause of morbidity and mortality in part due to secondary organ injury and infection. Yet, our ability to predict the downstr...
Accurate multidimensional sleep health (MSH) information is often fragmented and inconsistently represented within hospital infrastructures, leaving c...
In clinical settings, patients often express dissatisfaction through narrative speech or written text. However, most complaints management systems sti...
To evaluate whether machine learning models trained solely on administrative and demographic data can predict inpatient APR Risk of Mortality in diabe...
Hepatitis, a disease characterized by inflammation of the liver, is a leading global health challenge that contributes to over 1.3 million deaths annu...
Artificial intelligence (AI) has impacted healthcare at urban and academic medical centers globally. The current focus on AI deployments in urban area...
Magnetic Resonance Diffusion-Weighted Imaging (DWI) is a crucial tool for diagnosing acute ischemic stroke, yet some patients present as DWI-negative....
Information overload in electronic health records (EHRs) hampers clinicians’ ability to efficiently extract and synthesize critical information from a...
The early recognition of clinical deterioration in hospital inpatients continues to be a major challenge in healthcare. In this work, we proposed an i...
Carbapenem resistance in Pseudomonas aeruginosa is increasing in intensive care units (ICUs). To enhance antimicrobial stewardship and infection contr...