Knowledge Tree
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related 0.60 — strongly supporting 6 facts
Large Language Models are increasingly applied to the field of medicine through specialized frameworks and intelligent agent systems [1], though their integration faces challenges regarding logical consistency [2] and knowledge representation [3]. Academic research explores the progress and future path of these models in clinical settings {fact:1, fact:5}, often utilizing techniques like instruction tuning and retrieval-augmented generation to improve performance [4].
Facts (6)
Sources
Medical Hallucination in Foundation Models and Their ... medrxiv.org 1 fact
procedureResearchers adapt LLMs for medicine using domain-specific corpora, instruction tuning, and retrieval-augmented generation (RAG) to align outputs with clinical practice, as described by Wei et al. (2022) and Lewis et al. (2020).
Knowledge Graph Combined with Retrieval-Augmented Generation ... drpress.org 1 fact
claimIn specialized domains such as law, medicine, and science, text generation by Large Language Models (LLMs) often suffers from a lack of coherence and logical consistency, particularly when tasks require multi-hop reasoning and analysis.
Practices, opportunities and challenges in the fusion of knowledge ... frontiersin.org 1 fact
claimLarge-scale Knowledge Graphs often exhibit limited representation in specialized domains such as medicine and law, where many entities and relations are missing or weakly connected, creating a coverage gap and structural sparsity that limits their usefulness in tasks requiring nuanced domain-specific reasoning.
KG-RAG: Bridging the Gap Between Knowledge and Creativity - arXiv arxiv.org 1 fact
claimLarge Language Models are being utilized in intelligent agent systems for applications in medicine and finance, with notable frameworks including Langchain and LlamaIndex.
Bridging the Gap Between LLMs and Evolving Medical Knowledge arxiv.org 1 fact
referenceHongjian Zhou et al. (2023) published 'A survey of large language models in medicine: Progress, application, and challenge' as an arXiv preprint (arXiv:2311.05112).
A framework to assess clinical safety and hallucination rates of LLMs ... nature.com 1 fact
perspectiveRiedemann, Labonne, and Gilbert (2024) argue that the path forward for large language models in medicine is open, published in NPJ Digital Medicine.