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Found 9 papers, 3 papers with code
SLaVA-CXR: Small Language and Vision Assistant for Chest X-ray Report Automation
Inspired by the success of large language models (LLMs), there is growing research interest in developing LLMs in the medical domain to assist clinicians.
Integrating Knowledge Retrieval and Large Language Models for Clinical Report Correction
This study proposes an approach for error correction in radiology reports, leveraging large language models (LLMs) and retrieval-augmented generation (RAG) techniques.
Chain-of-Though (CoT) prompting strategies for medical error detection and correction
We report results for three methods of few-shot In-Context Learning (ICL) augmented with Chain-of-Thought (CoT) and reason prompts using a large language model (LLM).
MedExQA: Medical Question Answering Benchmark with Multiple Explanations
This paper introduces MedExQA, a novel benchmark in medical question-answering, to evaluate large language models' (LLMs) understanding of medical knowledge through explanations.
EB-GAME: A Game-Changer in ECG Heartbeat Anomaly Detection
Cardiologists use electrocardiograms (ECG) for the detection of arrhythmias.
Enhancing Human-Computer Interaction in Chest X-ray Analysis using Vision and Language Model with Eye Gaze Patterns
This work proposes a novel approach to enhance human-computer interaction in chest X-ray analysis using Vision-Language Models (VLMs) enhanced with radiologists' attention by incorporating eye gaze data alongside textual prompts.
Hallucination Benchmark in Medical Visual Question Answering
The recent success of large language and vision models (LLVMs) on vision question answering (VQA), particularly their applications in medicine (Med-VQA), has shown a great potential of realizing effective visual assistants for healthcare.
Exploring Multimodal Large Language Models for Radiology Report Error-checking
This paper proposes one of the first clinical applications of multimodal large language models (LLMs) as an assistant for radiologists to check errors in their reports.
Gesture Recognition with a Skeleton-Based Keyframe Selection Module
The BCCN consists of two pathways: (i) a keyframe pathway and (ii) a temporal-attention pathway.
