As the volume of medical literature accelerates, necessitating efficient tools to synthesize evidence for clinical practice and research, the interest in leveraging large language models (LLMs) for generating clinical reviews has surged. However, there…
Continue Reading
Demonstrating cardiovascular (CV) benefits with lipid-lowering therapy (LLT) requires long-term randomized clinical trials (RCTs) with thousands of patients. Innovative approaches such as in silico trials applying a disease computational model to virtual patients receiving…
Continue Reading
We live in interesting regulatory times. In January, a bill was introduced to the US Congress proposing that AI “can qualify as a practitioner eligible to prescribe drugs” if overseen by the States and…
Continue Reading
Differential diagnosis (DDx) is crucial for medicine as it helps healthcare providers systematically distinguish between conditions that share similar symptoms. This study evaluates the influence of lab test results on DDx accuracy generated by…
Continue Reading
Attention-deficit/hyperactivity disorder (ADHD), characterized by diagnostic complexity and symptom heterogeneity, is a prevalent neurodevelopmental disorder. Here, we explored the machine learning (ML) analysis of retinal fundus photographs as a noninvasive biomarker for ADHD screening…
Continue Reading
Large language models (LLMs) can answer expert-level questions in medicine but are prone to hallucinations and arithmetic errors. Early evidence suggests LLMs cannot reliably perform clinical calculations, limiting their potential integration into clinical workflows….
Continue Reading
Current clinician-based and automated risk assessment methods treat the risk of suicide-related behaviors (SRBs) as static, while in actual clinical practice, SRB risk fluctuates over time. Here, we develop two closely related model classes,…
Continue Reading
Large Language Models (LLMs) show promise in healthcare tasks but face challenges in complex medical scenarios. We developed a Multi-Agent Conversation (MAC) framework for disease diagnosis, inspired by clinical Multi-Disciplinary Team discussions. Using 302…
Continue Reading
A computer-aided detection (CAD) system for early esophagus carcinoma identification during endoscopy with narrow-band imaging (NBI) was evaluated in a large-scale, prospective, tandem, randomized controlled trial to assess its effectiveness. The study was registered…
Continue Reading
Low-dose computed tomography attenuation correction (CTAC) scans are used in hybrid myocardial perfusion imaging (MPI) for attenuation correction and coronary calcium scoring, and contain additional anatomic and pathologic information not utilized in clinical assessment….
Continue Reading
