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.

翻译：过去三到五年间，为医疗健康相关用途生成机器学习合成数据已成为可能。然而，人们也对人工数据集生成可能带来的负面因素表示担忧，包括生成式人工智能在网络安全犯罪等领域的潜在滥用、利用深度伪造和虚假新闻进行欺骗或操纵，以及在各市场领域对人类工作岗位的替代。本文探讨了合成数据集当前与未来积极的进展及应用前景。合成数据在数据隐私保护、科学研究、平衡数据集以及减少机器学习模型偏差方面具有显著优势。生成式人工智能能够通过生成模型创建文本、图像、视频或其他数据，其近期爆发式发展始于大型语言模型的发明与快速普及。这些基于Transformer架构的计算模型实现了通用语言生成及其他自然语言处理任务，相比先前神经网络架构实现了跨越式演进。随着生成式人工智能技术的改进与大规模应用，当前正是思考如何利用合成数据推动传染病研究的契机。本评述旨在系统阐述合成数据在传染病研究中的现状与未来定位。