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Found 10 papers, 7 papers with code
Towards Full Authorship with AI: Supporting Revision with AI-Generated Views
Large language models (LLMs) are shaping a new user interface (UI) paradigm in writing tools by enabling users to generate text through prompts.
CheX-GPT: Harnessing Large Language Models for Enhanced Chest X-ray Report Labeling
Moreover, models using expert-annotated data are limited by data scarcity and pre-defined classes, impacting their performance, flexibility and scalability.
CXR-CLIP: Toward Large Scale Chest X-ray Language-Image Pre-training
In this paper, we tackle the lack of image-text data in chest X-ray by expanding image-label pair as image-text pair via general prompt and utilizing multiple images and multiple sections in a radiologic report.
KG-GPT: A General Framework for Reasoning on Knowledge Graphs Using Large Language Models
While large language models (LLMs) have made considerable advancements in understanding and generating unstructured text, their application in structured data remains underexplored.
FactKG: Fact Verification via Reasoning on Knowledge Graphs
KGs can be a valuable knowledge source in fact verification due to their reliability and broad applicability.
Text-to-ECG: 12-Lead Electrocardiogram Synthesis conditioned on Clinical Text Reports
We compare the performance of our model with other representative models in text-to-speech and text-to-image.
Graph-Text Multi-Modal Pre-training for Medical Representation Learning
MedGTX uses a novel graph encoder to exploit the graphical nature of structured EHR data, and a text encoder to handle unstructured text, and a cross-modal encoder to learn a joint representation space.
Question Answering for Complex Electronic Health Records Database using Unified Encoder-Decoder Architecture
An intelligent machine that can answer human questions based on electronic health records (EHR-QA) has a great practical value, such as supporting clinical decisions, managing hospital administration, and medical chatbots.
Utilizing Graph Measure to Deduce Omitted Entities in Paragraphs
This demo deals with the problem of capturing omitted arguments in relation extraction given a proper knowledge base for entities of interest.
A Korean Knowledge Extraction System for Enriching a KBox
The increased demand for structured knowledge has created considerable interest in knowledge extraction from natural language sentences.
