Latest AI and machine learning research in hospital-based medicine for healthcare professionals.
OBJECTIVES: Large language models (LLMs) using a retrieval-augmented generation (RAG) approach have the ability to respond to user queries with answers grounded in specific sources. We conducted an exploratory evaluation of the accuracy of a RAG-based LLM to provide care recommendations for prehospital scenarios based on the emergency medical services (EMS) policies and treatment protocols (TPs). ...
Holderried and colleagues tested whether artificial intelligence (AI)-generated, patient-centered information can help people understand what they need to do after being discharged from a hospital. Participants demonstrated stronger comprehension when they viewed the simplified, patient-centered information rather than a standard letter. This work adds to the available early-phase evidence of AI s...
Pancreatic ductal adenocarcinoma remains one of the deadliest malignancies, characterized by late diagnosis, aggressive biology and limited therapeuti...
BACKGROUND: The expansion of digitalization in the pre-, intra- and post-operative surgical phases allow the development and integration of advanced t...
BACKGROUND: Heart failure (HF) remains a major cause of morbidity and mortality worldwide, and acute decompensation frequently necessitates intensive ...
BACKGROUND: The stress hyperglycemia ratio (SHR) and glycemic variability (GV) both reflect acute glycemic fluctuations, with established roles in car...
Radiology is often portrayed as the medical specialty most vulnerable to replacement by artificial intelligence (AI), potentially impacting medical st...
PURPOSE: This study aimed to evaluate the concordance between treatment recommendations generated by LLMs and decisions made by a multidisciplinary ur...
OBJECTIVES: To develop a random survival forest (RSF) machine learning (ML) model for predicting venous thromboembolism (VTE) risk in rheumatoid arthr...
This study aimed to develop an interpretable machine learning model for predicting in-hospital mortality among acute ischemic stroke (AIS) patients ad...
Artificial intelligence (AI) is progressively utilized in cardiology; nonetheless, the overarching advantages across various care domains remain ambig...
INTRODUCTION: Renal cell carcinoma (RCC) most commonly metastasizes to the lungs and shares risk factors with lung cancer. However, primary lung cance...
BACKGROUND: To strengthen preoperative preparation and improve clinical outcomes, a multimedia prehabilitation program was created for patients underg...
BACKGROUND: Monitoring antibiotic consumption and resistance is central to antimicrobial stewardship (AMS). However, this is particularly challenging ...
Abiotic stresses such as heat waves significantly reduce wheat productivity by altering leaf anatomy and physiology, leading to reduced photosynthetic...
The use of artificial intelligence (AI) offers significant potential to increase efficiency in hospitals, particularly in the context of demographic c...
When renal function is lost following resection of renal tumors, in the setting of end-stage kidney disease, or after traumatic nephrectomy, kidney tr...
Lung transplantation remains the only definitive treatment for end-stage respiratory failure; however, it has substantial post-operative mortality ris...
BACKGROUND: Surgical site infection after cardiac surgery is a common cause of morbidity and unplanned healthcare use, with most infections developing...