# Synthetic data: How could it be used for infectious disease research?

arXiv: https://arxiv.org/abs/2407.06211 · HTML: https://mlsanity.com/paper/2407.06211/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in LLM synthetic data generation, one of 13 directions in Synthetic data and self-improvement for LLMs.
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 3 Jul 2024.

## Placed in

- Synthetic data and self-improvement for LLMs / [LLM synthetic data generation](https://mlsanity.com/synth/llm-synthetic-data-generation/)

## Builds on (most cited first, 4 of 4)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Generative AI for Synthetic Data Generation: Methods, Challenges and the Future](https://mlsanity.com/paper/2403.04190/) (2024)
- [Conditional Synthetic Data Generation for Robust Machine Learning Applications with Limited Pandemic Data](https://mlsanity.com/paper/2109.06486/) (2021)
- [Synthesis of COVID-19 Chest X-rays using Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2010.10266/) (2020)

## Abstract (arXiv)

Over the last three to five years, it has become possible to generate machine learning synthetic data for healthcare-related uses. However, concerns have been raised about potential negative factors associated with the possibilities of artificial dataset generation. These include the potential misuse of generative artificial intelligence (AI) in fields such as cybercrime, the use of deepfakes and fake news to deceive or manipulate, and displacement of human jobs across various market sectors. Here, we consider both current and future positive advances and possibilities with synthetic datasets. Synthetic data offers significant benefits, particularly in data privacy, research, in balancing datasets and reducing bias in machine learning models. Generative AI is an artificial intelligence genre capable of creating text, images, video or other data using generative models. The recent explosion of interest in GenAI was heralded by the invention and speedy move to use of large language models (LLM). These computational models are able to achieve general-purpose language generation and other natural language processing tasks and are based on transformer architectures, which made an evolutionary leap from previous neural network architectures. Fuelled by the advent of improved GenAI techniques and wide scale usage, this is surely the time to consider how synthetic data can be used to advance infectious disease research. In this commentary we aim to create an overview of the current and future position of synthetic data in infectious disease research.