Background: Risk prediction in dilated cardiomyopathy ( #DCM ) remains suboptimal, and there is uncertainty about how newer machine-learning (ML) methods compare with conventional regression for clinically useful prognostic modelling. Advanced three-dimensional (3D) echocardiographic measures, particularly of right ventricular function, may improve model performance when combined with routinely collected clinical data. We aimed to compare… Continue reading Predicting adverse outcomes in dilated cardiomyopathy using 3D echocardiography: penalised Cox regression versus machine learning
Category: Tech
Machine learning models classifiers enable a strong prediction of radioembolization-induced liver disease, and define a new bilirubin threshold for selection of patients
Purpose: Selective Internal Radiotherapy (SIRT) is an established treatment option for hepatocellular carcinoma (#HCC ). However, a major complication is radioembolization-induced liver disease (REILD).Methods: This retrospective study, analyzed patients treated with SIRT for HCC to identify clinical factors associated with REILD and to predict treatment response. Machine learning (#ML ) methods were applied to two… Continue reading Machine learning models classifiers enable a strong prediction of radioembolization-induced liver disease, and define a new bilirubin threshold for selection of patients
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
Episode Charges and Subsequent Visits After Telemedicine vs In-Person Care
Importance: Telemedicine use increased during the COVID-19 pandemic and has remained a regular component of health care delivery. However, the financial implications of this change for health systems' reimbursement and utilization remain unclear.Objective: To compare 30-day episode charges and subsequent visits after telemedicine and in-person index visits.Design, setting, and participants: The target trial emulation conducted… Continue reading Episode Charges and Subsequent Visits After Telemedicine vs In-Person Care
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
Effective prompt design for large language models in clinical #practice
Large language models ( #LLMs ) have emerged as transformative healthcare tools for clinical documentation, diagnostic reasoning, and medical education. However, effective utilization requires understanding prompt engineering principles—the strategic design of inputs to optimize performance while mitigating hallucination, bias, and outdated information.MethodsThis narrative review synthesizes evidence from a structured PubMed search through December 2025 using… Continue reading Effective prompt design for large language models in clinical #practice
Comparison of verbal autopsy using a large #language model to biologically confirmed causes of death for #malaria and other communicable diseases among children in six sub-Saharan African countries
Malaria, a preventable parasitic disease, causes most child deaths in sub-Saharan Africa (SSA). Reliable cause-of-death data are essential to evaluate progress toward the national and global malaria control goals. However, civil registration and vital statistics are often weak and incomplete in many low- and middle-income countries. In such circumstances, verbal autopsy (VA) provides an alternative… Continue reading Comparison of verbal autopsy using a large #language model to biologically confirmed causes of death for #malaria and other communicable diseases among children in six sub-Saharan African countries
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