Applications of large language models (LLMs) like ChatGPT have potential to enhance clinical decision support through conversational interfaces. However, challenges of human-algorithmic interaction and clinician trust are poorly understood. GutGPT, a LLM for gastrointestinal (GI) bleeding risk prediction and management guidance, was deployed in clinical simulation scenarios alongside the electronic health record (EHR) with emergency medicine physicians, internal medicine physicians, and medical students to evaluate its effect on physician acceptance and trust in AI clinical decision support systems (AI-CDSS). GutGPT provides risk predictions from a validated machine learning model and evidence-based answers by querying extracted clinical guidelines. Participants were randomized to GutGPT and an interactive dashboard, or the interactive dashboard and a search engine. Surveys and educational assessments taken before and after measured technology acceptance and content mastery. Preliminary results showed mixed effects on acceptance after using GutGPT compared to the dashboard or search engine but appeared to improve content mastery based on simulation performance. Overall, this study demonstrates LLMs like GutGPT could enhance effective AI-CDSS if implemented optimally and paired with interactive interfaces.

A technician monitors a brain MRI scan ... [ ] session. Although Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common neurological conditions that affects children and adults, it is still widely misunderstood. ADHD symptoms are commonly misdiagnosed or remain undiagnosed -- particularly among girls and women. In a new study, researchers made a breakthrough by potentially finding a far more robust mechanism through which ADHD diagnosis via brain MRI scans might become a reality in the future. A team of three researchers at the Yale School of Medicine delved into the data from MRI tests that were conducted on 7,805 children based in the United States.

This well validated "machine learning" method is designed to avoid over-fitting to any specific data set and thereby increases the likelihood their findings …