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…
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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…
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Red teaming, the practice of adversarially exposing unexpected or undesired model behaviors, is critical towards improving equity and accuracy of large language models, but non-model creator-affiliated red teaming is scant in healthcare. We convened…
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The use of Large Language Models (LLMs) in mental health highlights the need to understand their responses to emotional content. Previous research shows that emotion-inducing prompts can elevate “anxiety” in LLMs, affecting behavior and…
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In this study, we present MedS-Bench, a comprehensive benchmark to evaluate large language models (LLMs) in clinical contexts, MedS-Bench, spanning 11 high-level clinical tasks. We evaluate nine leading LLMs, e.g., MEDITRON, Llama 3, Mistral,…
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Due to the large size and lack of fine-grained annotation, Whole Slide Images (WSIs) analysis is commonly approached as a Multiple Instance Learning (MIL) problem. However, previous studies only learn from training data, posing…
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Researchers concluded that the strong performance of GPT-4 large language models (LLMs) suggests their potential as a tool to help fill gaps in physicians’ knowledge and aid in diagnosing medical conditions in the future,…
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Marc Succi, MD, Founder and Executive Director of the MESH Incubator at Mass General
What You Should Know:
– A new study published in the Journal of Medical Artificial Intelligence explores the potential of large language…
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Autonomous Medical Evaluation for Guideline Adherence (AMEGA) is a comprehensive benchmark designed to evaluate large language models’ adherence to medical guidelines across 20 diagnostic scenarios spanning 13 specialties. It includes an evaluation framework and…
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