arXiv Machine Learning July 7, 2026 By Nitish Nagesh, Pengbao Zhou, Atchuth Naveen Chilaparasetti, Yajat Nagaraj Kiran, Tu Nguyen, Arshia Harish Puthran, Muhjaazee Love, Aadi Sharma, Mahdi Bagheri, Ian Harris, Amir M. Rahmani

Memisis: Orchestrating and Evaluating Synthetic Data for Tabular Health Datasets

Read the original on arXiv Machine Learning →

arXiv:2605. 17758v2 Announce Type: replace Abstract: Synthetic data is widely used in healthcare to create datasets that preserve statistical properties of real data without exposing sensitive patient information.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI

Jun 26

A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models

arXiv:2606. 26879v1 Announce Type: new Abstract: Synthetic data is increasingly used to enable the development and evaluation of AI systems in domains where access to real-world data is restricted.

By William Poulett

llms safety

Hugging Face Trending Papers

Jun 25

A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models

Synthetic data is increasingly used to enable the development and evaluation of AI systems in domains where access to real-world data is restricted. In healthcare, clinical documentation presents particular challenges due to its sensitivity.

llms safety

arXiv Machine Learning

Jun 8

Accelerating Reproducible Research in Synthetic EHR Generation

arXiv:2606. 06990v1 Announce Type: new Abstract: The generation of high-fidelity synthetic Electronic Health Records (EHR) is crucial for advancing medical research while preserving patient privacy.

By Jalen Jiang, Chufan Gao, Ethan Rasmussen, Stephen Z. Xie, Jimeng Sun

llms diffusion benchmarks safety

arXiv Machine Learning

Sep 1

Creation begins with understanding: LLMs as strategy designers for privacy-preserving tabular data synthesis

arXiv:2608.29674v1 Announce Type: new Abstract: Sharing tabular data in high-stakes domains is constrained by privacy regulations. Synthetic data offer a promising alternative, but deep generative mo...

By Jinmeng Li, Quan Zhang, Hangting Ye, He Zhao, Firas Laakom, Dandan Guo, J\"urgen Schmidhuber

llms safety

arXiv Computation and Language

Sep 22

MedGPT-oss: Training a General-Purpose Vision-Language Model for Biomedicine

arXiv:2603.00842v2 Announce Type: replace Abstract: Biomedical multimodal assistants have the potential to unify radiology, pathology, and clinical-text reasoning, yet a critical deployment gap remai...

By Kai Zhang, Zhengqing Yuan, Cheng Peng, Songlin Zhao, Mengxian Lyu, Ziyi Chen, Yanfang Ye, Wei Liu, Ying Zhang, Kaleb E Smith, Lifang He, Lichao Sun, Yonghui Wu

llms multimodal safety

arXiv Machine Learning

Aug 14

CoMedBench: A Multi-Source Benchmark of Synthetic Medical Data Fidelity and Downstream Utility

arXiv:2608. 12805v1 Announce Type: new Abstract: Access to clinical data is essential for developing reliable healthcare machine learning systems, but direct use of electronic health records is constrained by privacy regulation, institutional review, data-use agreements, and the risk of re-identification.

By Akanta Das, Al Amin Farhad, Mrinmoy Sarkar Anto, David Rehkopf, Ayin Vala, Tanmoy Sarkar Pias

benchmarks safety

One email a morning, machine-written

Related stories

A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models

A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models

Accelerating Reproducible Research in Synthetic EHR Generation

Creation begins with understanding: LLMs as strategy designers for privacy-preserving tabular data synthesis

MedGPT-oss: Training a General-Purpose Vision-Language Model for Biomedicine

CoMedBench: A Multi-Source Benchmark of Synthetic Medical Data Fidelity and Downstream Utility