Mass General Brigham researchers see value in a hybrid approach that makes use of generative artificial intelligence to diagnose patients. Comparing two arge language models – OpenAI’s GPT-4 and Google’s Gemini 1.5 – with its…
Continue Reading
The advent of large language models (LLMs) represents an enormous opportunity to revolutionize medical education. Via “synthetic education,” LLMs can be harnessed to generate novel content for medical education purposes, offering potentially unlimited resources…
Continue Reading
Although using artificial intelligence (AI) to analyze ultrasound images is a promising approach to assessing thyroid nodule risks, traditional AI models lack transparency and interpretability. We developed a multimodal generative pre-trained transformer for thyroid…
Continue Reading
Large language models generate plausible text responses to medical questions, but inaccurate responses pose significant risks in medical decision-making. Grading LLM outputs to determine the best model or answer is time-consuming and impractical in…
Continue Reading
Understanding reasons for treatment switching is of significant medical interest, but these factors are often only found in unstructured clinical notes and can be difficult to extract. We evaluated the zero-shot abilities of GPT-4…
Continue Reading
Seizure frequency is essential for evaluating epilepsy treatment, ensuring patient safety, and reducing risk for Sudden Unexpected Death in Epilepsy. As this information is often described in clinical narratives, this study presents an approach…
Continue Reading
1. What Is Retrieval-Augmented Generation?
RAG is a way to leverage already-trained large language models such as GPT-4, Gemini, Bard and Llama when building applications powered by artificial intelligence. By adding local knowledge (such as…
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
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
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
