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This study compared 11 AI platforms, including large language models and retrieval-augmented systems, for diagnosing and managing periodontal diseases using clinical vignettes rated by expert periodontists.
This study evaluates large language model (LLM) performance for predicting same-day hospital discharge, identifies primary error sources, and demonstrates that automated prompt optimization significantly improves predictive accuracy.
A study benchmarking large language models in gynecologic oncology demonstrates that guideline-anchored retrieval-augmented generation (RAG) configurations outperform baseline and literature-only AI systems in clinical decision support, hig
A comprehensive meta-analysis indicates that rule-based chatbots provide a modest but statistically significant reduction in depressive symptoms within a 4-8 week window, whereas evidence for the clinical efficacy of Large Language Model (L
This review evaluates AI agent systems built on large language models in healthcare, demonstrating improved clinical task accuracy over base models, especially in complex scenarios. Multi-agent architectures show promise but require further
A study comparing large language model-generated versus physician-generated discharge summaries found comparable quality and safety, suggesting AI-assisted documentation could reduce clinical burden without compromising patient care.
A landmark study quantitatively benchmarks a multi-agent LLM-based AI system against board-certified clinicians in real-world virtual acute care, revealing near-equivalent diagnostic and therapeutic performance with superior documentation s
