# A Review on Generative AI Models for Synthetic Medical Text, Time Series, and Longitudinal Data arXiv: https://arxiv.org/abs/2411.12274 ยท HTML: https://mlsanity.com/paper/2411.12274/ ## Key facts - Cited by 10 papers in the map (data through 14 Oct 2026). - Builds on 28 of its 127 references that are in the map. - First posted on arXiv 19 Nov 2024. ## Builds on (most cited first, 28 of 28) - [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022) - [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020) - [Towards Reasoning in Large Language Models: A Survey](https://mlsanity.com/paper/2212.10403/) (2022) - [Texygen: A Benchmarking Platform for Text Generation Models](https://mlsanity.com/paper/1802.01886/) (2018) - [The UCR Time Series Archive](https://mlsanity.com/paper/1810.07758/) (2018) - [Generating Multi-label Discrete Patient Records using Generative Adversarial Networks](https://mlsanity.com/paper/1703.06490/) (2017) - [How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models](https://mlsanity.com/paper/2102.08921/) (2021) - [A Study of Generative Large Language Model for Medical Research and Healthcare](https://mlsanity.com/paper/2305.13523/) (2023) - [Large Language Models to Identify Social Determinants of Health in Electronic Health Records](https://mlsanity.com/paper/2308.06354/) (2023) - [UniMiB SHAR: a new dataset for human activity recognition using acceleration data from smartphones](https://mlsanity.com/paper/1611.07688/) (2016) - [Generating Synthetic Mixed-type Longitudinal Electronic Health Records for Artificial Intelligent Applications](https://mlsanity.com/paper/2112.12047/) (2021) - [TTS-GAN: A Transformer-based Time-Series Generative Adversarial Network](https://mlsanity.com/paper/2202.02691/) (2022) - [HiRID-ICU-Benchmark -- A Comprehensive Machine Learning Benchmark on High-resolution ICU Data](https://mlsanity.com/paper/2111.08536/) (2021) - [Diffusion-based Conditional ECG Generation with Structured State Space Models](https://mlsanity.com/paper/2301.08227/) (2023) - [Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review](https://mlsanity.com/paper/2107.02975/) (2021) - [AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks](https://mlsanity.com/paper/2306.08107/) (2023) - [Foundation Metrics for Evaluating Effectiveness of Healthcare Conversations Powered by Generative AI](https://mlsanity.com/paper/2309.12444/) (2023) - [Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic Data](https://mlsanity.com/paper/2304.03722/) (2023) - [Natural Language Generation for Electronic Health Records](https://mlsanity.com/paper/1806.01353/) (2018) - [PromptEHR: Conditional Electronic Healthcare Records Generation with Prompt Learning](https://mlsanity.com/paper/2211.01761/) (2022) - [Conditional Generation of Medical Time Series for Extrapolation to Underrepresented Populations](https://mlsanity.com/paper/2201.08186/) (2022) - [A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources](https://mlsanity.com/paper/2203.07018/) (2022) - [Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy](https://mlsanity.com/paper/2306.07999/) (2023) - [A Comprehensive Survey on Generative Diffusion Models for Structured Data](https://mlsanity.com/paper/2306.04139/) (2023) - [Augmenting Physiological Time Series Data: A Case Study for Sleep Apnea Detection](https://mlsanity.com/paper/1905.09068/) (2019) - [Synthesizing time-series wound prognosis factors from electronic medical records using generative adversarial networks](https://mlsanity.com/paper/2105.01159/) (2021) - [ECG synthesis with Neural ODE and GAN models](https://mlsanity.com/paper/2111.00314/) (2021) - [Accurate Detection of Paroxysmal Atrial Fibrillation with Certified-GAN and Neural Architecture Search](https://mlsanity.com/paper/2301.10173/) (2023) ## Built on by (most cited first, 10 of 10) - [A Scoping Review of Synthetic Data Generation by Language Models in Biomedical Research and Application: Data Utility and Quality Perspectives](https://mlsanity.com/paper/2506.16594/) (2025) - [Diffusion models recover accurate mixture weights despite score function insensitivity](https://mlsanity.com/paper/2607.15485/) (2026) - [Using matrix-product states for time-series machine learning](https://mlsanity.com/paper/2412.15826/) (2024) - [PersonaBOT: Bringing Customer Personas to Life with LLMs and RAG](https://mlsanity.com/paper/2505.17156/) (2025) - [Diffusion-Based Electrocardiography Noise Quantification via Anomaly Detection](https://mlsanity.com/paper/2506.11815/) (2025) - [Longitudinal NSCLC Treatment Progression via Multimodal Generative Models](https://mlsanity.com/paper/2603.06147/) (2026) - [Democratising Clinical AI through Dataset Condensation for Classical Clinical Models](https://mlsanity.com/paper/2603.09356/) (2026) - [ESL-Bench: An Event-Driven Synthetic Longitudinal Benchmark for Health Agents](https://mlsanity.com/paper/2604.02834/) (2026) - [Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies](https://mlsanity.com/paper/2608.05993/) (2026) - [Large language models in medical time series analysis](https://mlsanity.com/paper/2609.22262/) (2026) ## Abstract (arXiv) This paper presents the results of a novel scoping review on the practical models for generating three different types of synthetic health records (SHRs): medical text, time series, and longitudinal data. The innovative aspects of the review, which incorporate study objectives, data modality, and research methodology of the reviewed studies, uncover the importance and the scope of the topic for the digital medicine context. In total, 52 publications met the eligibility criteria for generating medical time series (22), longitudinal data (17), and medical text (13). Privacy preservation was found to be the main research objective of the studied papers, along with class imbalance, data scarcity, and data imputation as the other objectives. The adversarial network-based, probabilistic, and large language models exhibited superiority for generating synthetic longitudinal data, time series, and medical texts, respectively. Finding a reliable performance measure to quantify SHR re-identification risk is the major research gap of the topic.