Graph retrieval-augmented generation: A survey
Recently, Retrieval-Augmented Generation (RAG) has achieved remarkable success in
addressing the challenges of Large Language Models (LLMs) without necessitating …
addressing the challenges of Large Language Models (LLMs) without necessitating …
A causal explainable guardrails for large language models
Large Language Models (LLMs) have shown impressive performance in natural language
tasks, but their outputs can exhibit undesirable attributes or biases. Existing methods for …
tasks, but their outputs can exhibit undesirable attributes or biases. Existing methods for …
Visual Question Answering in Robotic Surgery: A Comprehensive Review
D Ding, T Yao, R Luo, X Sun - IEEE Access, 2025 - ieeexplore.ieee.org
Visual Question Answering (VQA) in robotic surgery is rapidly becoming a pivotal
technology in medical AI, addressing the complex challenge of interpreting multimodal …
technology in medical AI, addressing the complex challenge of interpreting multimodal …
ClinicalBench: Can LLMs Beat Traditional ML Models in Clinical Prediction?
Large Language Models (LLMs) hold great promise to revolutionize current clinical systems
for their superior capacities on medical text processing tasks and medical licensing exams …
for their superior capacities on medical text processing tasks and medical licensing exams …
Prompt-Consistency Image Generation (PCIG): A Unified Framework Integrating LLMs, Knowledge Graphs, and Controllable Diffusion Models
The rapid advancement of Text-to-Image (T2I) generative models has enabled the synthesis
of high-quality images guided by textual descriptions. Despite this significant progress, these …
of high-quality images guided by textual descriptions. Despite this significant progress, these …
Piors: Personalized intelligent outpatient reception based on large language model with multi-agents medical scenario simulation
Z Bao, Q Liu, Y Guo, Z Ye, J Shen, S **e, J Peng… - arxiv preprint arxiv …, 2024 - arxiv.org
In China, receptionist nurses face overwhelming workloads in outpatient settings, limiting
their time and attention for each patient and ultimately reducing service quality. In this paper …
their time and attention for each patient and ultimately reducing service quality. In this paper …
Building a literature knowledge base towards transparent biomedical AI
Y Huang, Z Han, X Luo, X Luo, Y Gao, M Zhao, F Tang… - bioRxiv, 2024 - biorxiv.org
As artificial intelligence (AI) continues to advance and scale up in biomedical research,
concerns about AI's trustworthiness and transparency have grown. There is a critical need to …
concerns about AI's trustworthiness and transparency have grown. There is a critical need to …
DALL-M: Context-Aware Clinical Data Augmentation with LLMs
X-ray images are vital in medical diagnostics, but their effectiveness is limited without clinical
context. Radiologists often find chest X-rays insufficient for diagnosing underlying diseases …
context. Radiologists often find chest X-rays insufficient for diagnosing underlying diseases …
OrthoDoc: Multimodal Large Language Model for Assisting Diagnosis in Computed Tomography
Y **, Y Zhang - arxiv preprint arxiv:2409.09052, 2024 - arxiv.org
Multimodal large language models (MLLMs) have achieved significant success in the
general field of image processing. Their emerging task generalization and freeform …
general field of image processing. Their emerging task generalization and freeform …
Graph of Logic: Enhancing LLM Reasoning with Graphs and Symbolic Logic
F Alotaibi, A Kulkarni, D Zhou - 2024 IEEE International …, 2024 - ieeexplore.ieee.org
Large language models (LLMs) have been shown to struggle with complex logical
reasoning tasks due to the inherent ambiguity and complexity of natural language. These …
reasoning tasks due to the inherent ambiguity and complexity of natural language. These …