融合扩散模型与大语言模型，生成逼真医疗图文数据解决数据短缺问题。
An Integrated Approach to AI-Generated Content in e-health
- 构建端到端条件生成框架，结合扩散模型与大语言模型生成医疗图像与文本。
- 合成图像性能超越传统GAN，未受控大模型生成文本更贴近真实语调。
- 适用于医学影像识别、皮肤病检测及心理健康评估等真实场景。
生成式人工智能在电子健康领域具有巨大潜力，可生成多样化数据。本文提出一种端到端的类别条件框架，通过生成合成医学图像和文本数据缓解健康应用中的数据稀缺问题，应用于视网膜病变检测、皮肤感染识别和心理健康评估等实际任务。该框架融合扩散模型与大语言模型（LLMs），生成的数据能精准匹配真实世界模式，有助于提升下游任务性能与模型鲁棒性。实验表明，所提出的扩散模型生成的合成图像优于传统GAN架构；在文本模态中，未受控的大语言模型生成的数据在语调真实性上显著优于受控模型。
原文摘要 · Abstract (English)
Artificial Intelligence-Generated Content, a subset of Generative Artificial Intelligence, holds significant potential for advancing the e-health sector by generating diverse forms of data. In this paper, we propose an end-to-end class-conditioned framework that addresses the challenge of data scarcity in health applications by generating synthetic medical images and text data, evaluating on practical applications such as retinopathy detection, skin infections and mental health assessments. Our framework integrates Diffusion and Large Language Models (LLMs) to generate data that closely match real-world patterns, which is essential for improving downstream task performance and model robustness in e-health applications. Experimental results demonstrate that the synthetic images produced by the proposed diffusion model outperform traditional GAN architectures. Similarly, in the text modality, data generated by uncensored LLM achieves significantly better alignment with real-world data than censored models in replicating the authentic tone.
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