An Integrated Approach to AI-Generated Content in e-health
This addresses data scarcity for e-health applications like retinopathy detection and mental health assessments, but it is incremental as it combines existing methods (Diffusion and LLMs) in a new framework.
The paper tackles data scarcity in e-health by proposing an end-to-end class-conditioned framework that generates synthetic medical images and text data using Diffusion and Large Language Models (LLMs), with results showing the diffusion model outperforms traditional GANs for images and uncensored LLMs achieve better alignment with real-world data for text.
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.