Background: The sustainability of service quality in healthcare systems is directly related to accurate resource planning, especially in #emergency departments with high unpredictability. This study aims to analyze the impact of meteorological factors on emergency department visits and propose a highly accurate and explainable artificial intelligence-based decision support model for hospital management. Within the scope… Continue reading Data-driven decision support in hospital resource planning: an artificial intelligence-based model proposal for emergency department demand
Tag: artificial-intelligence
Using randomization to compare #AI and expert-generated formative assessment questions in medical education
Background: AI-generated content is being used across the education spectrum and is beneficial for creating complex multiple-choice questions, such as those used in medical education. However, evaluating AI-generated content is challenging, and existing testing and evaluation methods are falling short. This study uses randomization to compare medical students' performance on and subjective evaluation of AI… Continue reading Using randomization to compare #AI and expert-generated formative assessment questions in medical education
From Advice to Action — Real-World Behavior of Patients Using an Integrated Diagnostic Decision Support System for Navigating the Health Care System
Artificial intelligence (#AI )–powered digital front door tools are increasingly being used to guide patients to appropriate care and alleviate health care system pressure. However, most evaluations offer limited insight into stated care intent, real-world behavior, or care appropriateness.MethodsThe E-Health Self–Symptom Assessment as a Front Door and Facilitator of Care (ESSENCE) study was a prospective… Continue reading From Advice to Action — Real-World Behavior of Patients Using an Integrated Diagnostic Decision Support System for Navigating the Health Care System
Vulnerability of Large Language Models to Prompt Injection When Providing Medical Advice
Importance Large language models (#LLMs ) are increasingly integrated into health care applications; however, their vulnerability to prompt-injection attacks (ie, maliciously crafted inputs that manipulate an LLM’s behavior) capable of altering medical recommendations has not been systematically evaluated.Objective To evaluate the susceptibility of commercial LLMs to prompt-injection attacks that may induce unsafe clinical advice and… Continue reading Vulnerability of Large Language Models to Prompt Injection When Providing Medical Advice
Artificial intelligence assisted colorectal lesion detection in private practices a randomized controlled study
Computer-aided #colonoscopy (CAC) may improve polyp detection and characterization compared to traditional colonoscopy (TC). However, recent studies also reported no relevant effect on #adenoma detection rate (ADR). This study evaluates the real-time #polyp detection system EndoMind during screening and surveillance colonoscopy in a multicenter randomized controlled trial. From November 2021 to November 2022, 933 individuals… Continue reading Artificial intelligence assisted colorectal lesion detection in private practices a randomized controlled study
The Role of Artificial Intelligence in Diagnosing Pulmonary Embolism: A Systematic Review and Meta-analysis
Introduction: Missed or delayed diagnosis of pulmonary embolism (#PE ) is associated with increased morbidity, mortality, and longer hospitalizations. This study aimed to evaluate the diagnostic accuracy of Artificial Intelligence (#AI ) models in detecting PE across imaging.Methods: We systematically searched PubMed/MEDLINE, Scopus, Embase and Web of Science from inception to 1 January 2025 without… Continue reading The Role of Artificial Intelligence in Diagnosing Pulmonary Embolism: A Systematic Review and Meta-analysis
How to predict abnormal acid reflux: recent developments
IntroductionRecent advances in physiology and technology have led to the identification of additional parameters that have the potential to enhance diagnostic accuracy and inform the management of Gastroesophageal reflux disease ( #GERD ). Whilst traditional pH monitoring and acid exposure time (AET) remain central to diagnosis, recent advances have introduced novel physiological markers that improve… Continue reading How to predict abnormal acid reflux: recent developments
Beyond human gold standards: A multimodel framework for automated abstract classification and information extraction
Meta-research and evidence synthesis require considerable resources. Large language models (#LLMs ) have emerged as promising tools to assist in these processes, yet their performance varies across models, limiting their reliability. Taking advantage of the large availability of small size (<10 billion parameters) open-source LLMs, we implemented an agreement-based framework in which a decision is… Continue reading Beyond human gold standards: A multimodel framework for automated abstract classification and information extraction
Human-large language model collaboration in clinical medicine: a systematic review and meta-analysis
Human- #AI collaboration (H + AI) using large language models ( #LLMs ) offers a promising approach to enhance clinical reasoning, documentation, and interpretation tasks. Following PRISMA 2020 (PROSPERO registration: CRD420251068272), we systematically compared H + AI with human-only (H) workflows, searching four databases through June 28, 2025. Ten peer-reviewed studies met eligibility criteria, with… Continue reading Human-large language model collaboration in clinical medicine: a systematic review and meta-analysis
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