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.
By Stig Hellemans, Tom Stroobants, Elyne Scheurwegs, Pieter Meysman, Philippe G. Jorens, Kris Laukens
arXiv:2607. 28788v1 Announce Type: new Abstract: Clinical diagnosis at hospital admission must be made rapidly from limited, incomplete evidence.
By Jiahui Li, Ruili Fang, Zishuai Liu, Yutong Guo, Nan Yang, Wenzhan Song, Jin Lu, Fei Dou
arXiv:2605. 03301v2 Announce Type: replace-cross Abstract: De-identification of clinical text is a prerequisite for the secondary use of electronic health records.
By Jose D. Posada, David Love, Somalee Datta, Priya Desai
The paper introduces TRACE, a method that removes duplicated text—known as note bloat—from clinical notes by leveraging EHR metadata and frequency-based de‑duplication. Across 5.3 million notes from diverse patient cohorts, TRACE eliminated 47.3 % of chart text while preserving information extraction and prediction performance, with only 0.3–6.6 % of removed content being author‑generated. The authors project that applying TRACE could yield net savings of $1.00 M to $13.58 M over three years at a large academic center, depending on model pricing schemes.
By Jordan L. Cahoon, Chloe Stanwyck, Asad Aali, Rachel Madding, Sulaiman S. Somani, Emma Sun, Yixing Jiang, Renumathy Dhanasekaran, Emily Alsentzer
The paper introduces a tool‑augmented large language model framework that improves the reliability of medical calculations derived from free‑form patient notes. It separates clinical language understanding from rule execution by retrieving calculator specifications, generating executable tools, and using a constrained extractor to map notes to calculator inputs. On the MedCalc‑Bench dataset, the method achieves 86.73% final‑answer accuracy, outperforming one‑shot prompting and fine‑tuned baselines by 24.0 and 17.27 percentage points.
By Yang Zhong, Ling Luo, Xuanlyu Zhu, Yanhua Wang, Yuanyuan Sun, Jian Wang, Hongfei Lin
MedPsy introduces a family of 1.7B and 4B text‑only medical language models optimized for edge deployment, combining synthetic medical data, chain‑of‑thought targets from a 235B teacher, and a four‑stage post‑training curriculum. On seven closed‑ended medical benchmarks, MedPsy‑4B outperforms MedGemma‑27B while being 6.75× smaller, and MedPsy‑1.7B surpasses MedGemma‑1.5‑4B by 11.42 points despite being less than half its size. The models also reduce response length, disk footprint, and end‑to‑end latency on real‑device inference, keeping peak RAM below 5 GB on laptops and smartphones.
By Davide Vitabile, Alexandro Buffa, Akshay Nambiar, Amril Nazir
The paper introduces Clinical System One (CS1), a 4‑billion‑parameter decision model that rapidly produces typed judgments from clinical records. Trained on 12.4 million Q&A pairs from 3.6 million patients, CS1 can answer multiple questions in a single forward pass, providing probability estimates for uncertain cases. In evaluations on 963,408 held‑out patients and 21 external datasets, CS1 outperformed larger open models and, when paired with a language‑model agent, efficiently identified trial‑eligible patients with far fewer tokens.
By Andrew Zhang, Trinh T. L. Vuong, Sophia J. Wagner, Dongmin Bang, Tong Ding, Yuchang Su, Mary Cipperman, Christina S. Chen, Judith Lefkes, Ramin Kahidi, Ming Y. Lu, Caiwei Tian, Tianhao Luo, Jingnan Ma, Rowland Pettit, Long Phi Le, Faisal Mahmood
The study evaluates whether large language models (LLMs) with in‑context learning can better identify institution‑specific protected health information (PHI) in electronic health records than existing de‑identification systems. Using 100 pediatric oncology notes from Texas Children’s Hospital, eight LLMs were compared to two purpose‑built systems and pattern‑based baselines under three progressively specific prompts. The best LLM achieved an F1 score of 0.918, recovering 79% of previously missed PHI categories and reaching a recall of 0.981 after iterative prompt refinement, demonstrating that calibrated single‑pass prompting can close the institutional PHI gap while balancing precision and recall.
By Daniel Palacios, Matthew Brady Neeley, Angel Adetomike Otto, Shalini Dhamodharan, John P. Woodhouse, Chi-fan Lin, Mark Zobeck, Zhandong Liu, Hyun-Hwan Jeong
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...
By Jakir Hossain, Yi-Fei Zhao, Hongjian Wang, Minmei Shih, Katie Leigh Mullen, Ahmad P. Tafti, Leming Zhou, Manoj Purohit, William Hogan, Jay Zeng, Elizabeth Skidmore, Yanshan Wang
arXiv:2610.07224v1 Announce Type: cross
Abstract: Clinical notes capture most of what is documented about a patient's care, but they cannot be used for research until protected health information (PH...
By Jose D. Posada, Somalee Datta, Priya Desai
The paper demonstrates that large language models (LLMs) used to preliminarily assess medical device documentation can produce inconsistent results across different commercial deployments and settings. By constructing a fully deterministic pipeline—including OCR, chunking, embedding, indexing, retrieval, and inference—the authors achieved byte‑identical outputs across repeated runs, varied machines, and even interruptions. The study shows that end‑to‑end determinism is possible and provides a verifiable foundation for audit and change control in regulatory compliance.
By Ryosuke Suzuki
arXiv:2607. 20453v1 Announce Type: cross Abstract: Large language models show promise for clinical prediction, but zero-shot performance on specialized tasks is limited by incomplete domain knowledge, especially for smaller locally deployable models.
By Jessica Sena, Shesadree Priyadarshani, Miguel Contreras, Bharat Gandhi, Scott Siegel, Subhash Nerella, Parisa Rashidi