An Integrated Approach to AI-Generated Content in e-health

TL;DR

This paper introduces an integrated AI framework combining diffusion models and large language models to generate synthetic medical data, improving e-health applications like disease detection and mental health assessment.

Contribution

It presents a novel end-to-end class-conditioned framework that enhances synthetic data quality for medical imaging and text, addressing data scarcity in e-health.

Findings

Synthetic images outperform traditional GANs.

Uncensored LLM-generated text better matches real data.

Framework improves downstream task performance.

Abstract

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.