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.
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.
arXiv:2609.22164v1 Announce Type: cross Abstract: Structured EHR is abundant but sparse, coded, and difficult to use directly for note-centric clinical modeling. We present MedNotes, a multi-agent sy...
The paper introduces Term2Note, a method for generating full-length clinical notes under differential privacy constraints. It separates content and form, conditioning note sections on medical terms and applying distinct DP protections to terms and notes, followed by a DP quality maximizer. Experiments show that the synthetic notes closely match real clinical notes in statistical properties, and models trained on them perform comparably to those trained on real data, outperforming existing DP text generation baselines.
arXiv:2608.23248v1 Announce Type: cross Abstract: Traditional clinical prediction models rely on task-specific pipelines and curated, structured data, which scale poorly and underutilize unstructured...
arXiv:2508. 01401v2 Announce Type: replace-cross Abstract: Physicians spend significant time documenting clinical encounters, a burden that contributes to professional burnout.
arXiv:2609.22239v1 Announce Type: new Abstract: Ambient AI is increasingly adopted in healthcare to automatically generate clinical notes from patient-clinician conversations, with the potential to s...
MedDeID is an on‑premises framework that combines in‑house annotation, synthetic‑note generation, model training, inference, pseudonymisation and evaluation to de‑identify clinical text. On a Dutch hospital benchmark, a hospital‑trained transformer detected 98.9 % of identifying text while redacting only 0.24 % of non‑identifier text; a synthetic‑only model achieved 96.1 %. In primary‑care notes, the synthetic‑trained model outperformed the hospital‑trained model in recall and robustness to identifier‑format changes, and an English version trained without real text reached 99.7 % and 98.9 % detection on synthetic benchmarks.
arXiv:2608.24327v2 Announce Type: replace Abstract: With the advent of Large Language Models and its instruction following capabilities a promising application is the task of summarization. Within th...
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.
The paper introduces a clinically grounded privacy evaluation framework for medical language models, assessing leakage across a spectrum of adversarial access levels—from publicly inferable demographics to leaked note fragments. Using this framework on an LM pretrained on 378,000 clinical notes, the authors find that routine encounter metadata leads to high verbatim memorization and significant recovery of sensitive diagnoses (e.g., AUROC 0.91 for abortion, 0.82 for HIV). They also note that exact-match memorization can overstate disclosure, with 36% of memorized tokens being templated documentation, underscoring the risks of training on longitudinal clinical data and offering a reusable evaluation tool.
arXiv:2605. 30295v2 Announce Type: replace-cross Abstract: Large language models (LLMs) show promise for clinical reasoning and decision support, but evaluation in realistic, electronic health record-congruent settings remains limited.
The paper introduces MIMIC-DOS, a dataset derived from MIMIC-IV that focuses on ICU cases where patient symptoms and medical signs are discordant. It presents CARE, a privacy‑compliant multi‑stage agentic reasoning framework that uses a proprietary LLM to generate structured categories and transitions, while a local LLM performs evidence acquisition and decision‑making. In retrospective evaluations on MIMIC‑DOS, CARE outperforms other LLMs and agentic workflows, demonstrating stronger handling of conflicting clinical evidence while preserving patient privacy.