IMACT-CXR presents an integrated multi-agent system using AutoGen, Bayesian Knowledge Tracing, gaze feedback, and vision-language models to provide interactive tutoring for chest X-ray interpretation with preliminary evidence of improved learner performance.
arXiv preprint arXiv:2405.13055 , year=
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UNVERDICTED 4representative citing papers
M4CXR is a multi-modal large language model that performs multiple tasks in chest X-ray analysis including report generation with claimed SOTA clinical accuracy using chain-of-thought prompting.
UniReason-Med introduces a unified framework for 2D and 3D medical VQA with shared grounded reasoning, trained on a 220K dataset, claiming that joint 2D+3D supervision improves 3D performance over 3D-only training.
GPT-4o and Claude 3.5 Sonnet reach 73.7-74% accuracy on gastroenterology questions; VLMs gain nothing from images and lose accuracy with LLM-generated captions.
citing papers explorer
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IMACT-CXR: An Interactive Multi-Agent Conversational Tutoring System for Chest X-Ray Interpretation
IMACT-CXR presents an integrated multi-agent system using AutoGen, Bayesian Knowledge Tracing, gaze feedback, and vision-language models to provide interactive tutoring for chest X-ray interpretation with preliminary evidence of improved learner performance.
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M4CXR: Exploring Multi-task Potentials of Multi-modal Large Language Models for Chest X-ray Interpretation
M4CXR is a multi-modal large language model that performs multiple tasks in chest X-ray analysis including report generation with claimed SOTA clinical accuracy using chain-of-thought prompting.
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UniReason-Med: A Shared Grounded Reasoning Interface for 2D-to-3D Transfer in Medical VQA
UniReason-Med introduces a unified framework for 2D and 3D medical VQA with shared grounded reasoning, trained on a 220K dataset, claiming that joint 2D+3D supervision improves 3D performance over 3D-only training.
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Vision-Language and Large Language Model Performance in Gastroenterology: GPT, Claude, Llama, Phi, Mistral, Gemma, and Quantized Models
GPT-4o and Claude 3.5 Sonnet reach 73.7-74% accuracy on gastroenterology questions; VLMs gain nothing from images and lose accuracy with LLM-generated captions.