Latest AI and machine learning research in hospital-based medicine for healthcare professionals.
Anesthesiology has a longstanding commitment to patient safety, characterized by innovative research, quality improvement, multidisciplinary collaboration, and engineering-based approaches to care systems. The field has been instrumental in advancing technological developments across the perioperative continuum, contributing to the ongoing mission of harm reduction and risk mitigation. However, mo...
Large language models (LLMs) are increasingly used in clinical decision support, yet current evaluation methods often fail to distinguish genuine medical reasoning from superficial patterns. We introduce DeVisE (Demographics and Vital signs Evaluation), a behavioral testing framework for probing fine-grained clinical understanding. We construct a dataset of ICU discharge notes from MIMIC-IV, gen...
The Achilles heel of Large Language Models (LLMs) is hallucination, which has drastic consequences for the clinical domain. This is particularly imp...
Objective: To evaluate whether preoperative body composition metrics automatically extracted from CT scans can predict postoperative outcomes after ...
Objective: To evaluate whether preoperative body composition metrics automatically extracted from CT scans can predict postoperative outcomes after ...
Viruses are the most abundant biological entities on Earth and play central roles in shaping microbiomes and influencing ecosystem functions. Yet, m...
BACKGROUND: This study aimed to develop a deep learning model (DLM) for rapid screening of coronary heart disease (CHD) using "pseudo-normal" electroc...
In this paper, we introduce knowledge image generation as a new task, alongside the Massive Multi-Discipline Multi-Tier Knowledge-Image Generation B...
Predicting epileptic seizures presents a substantial difficulty in healthcare, with considerable implications for enhancing patient outcomes and quali...
The integration of deep learning-based glaucoma detection with large language models (LLMs) presents an automated strategy to mitigate ophthalmologi...
PURPOSE: Digital health or digital healthcare, is a broad, multidisciplinary concept that includes concepts from an intersection between technology an...
Postoperative stroke remains a critical complication in elderly surgical intensive care unit (SICU) patients, contributing to prolonged hospitalizat...
Through the analysis of multidimensional vibration signals of machinery, existing faults in mechanical equipment can be timely identified to ensure no...
Multimodal artificial intelligence (AI) is a powerful new technological advance, capable of simultaneously learning from diverse data types, such as t...
OBJECTIVE: Postpartum depression (PPD) is a major contributor to postpartum morbidity and mortality. Beyond efforts at routine screening, risk stratif...
According to forecasts, 27 % of the German population will be aged 65 or over by 2050. Age-associated multimorbidity, functional impairment and need f...
Electronic Health Record (EHR) is the digital form of patient visits that contains various medical data, including diagnosis, treatment, and lab event...
Causal effect estimation of individual heterogeneity is a core issue in the field of causal inference, and its application in medicine poses an active...
BACKGROUND: Personal protective equipment (PPE) is a first-line transmission-based precaution for reducing the spread of nosocomial infections between...
OBJECTIVE: Deep learning approaches have demonstrated significant potential in predicting temporal health events in recent years. However, existing me...