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
The study investigates whether cross‑lingual clinical annotation projection can be treated as a constrained text‑generation task that preserves the original text while inserting entity tags. Using a workflow that embeds tags directly into immutable target‑language text and then validates them deterministically, the authors evaluated this approach against supervised candidate‑span projection and hybrid ML‑LLM refinement across six languages. Results show that direct LLM projection, particularly with GLM 5.2 and Gemma4:31B, achieves the highest strict F1 scores (up to 0.9201) and outperforms previous methods by 0.0564–0.1512, producing over 55,000 grounded mentions with accurate offsets.
By \'Alvaro Rey-Blanes, Francisco J. Moreno-Barea, Francisco J. Veredas
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
arXiv:2605. 04221v2 Announce Type: replace-cross Abstract: Clinical named entity recognition from dental progress notes is challenging because documentation is highly unstructured, domain-specific, and often privacy-sensitive.
By Yao-Shun Chuang, Tushti Mody, Uday Pratap Singh, Shirindokht Shiraz, Chun-Teh Lee, Ryan Brandon, Muhammad F Walji, Xiaoqian Jiang, Bunmi Tokede
arXiv:2606. 12569v1 Announce Type: cross Abstract: We present EDEN (Emergency Department Electronic Notes), a new and unique large-scale corpus of clinical notes produced in Emergency Departments of Italian hospitals.
By Tiziano Labruna, Guido Bertolini, Pietro Ferrazzi, Bernardo Magnini
arXiv:2606. 12569v2 Announce Type: replace-cross Abstract: We present eCream-MedCorpus, a new and unique large-scale dataset of clinical notes produced in Emergency Departments of Italian hospitals.
By Tiziano Labruna, Guido Bertolini, Pietro Ferrazzi, Bernardo Magnini
arXiv:2606. 12569v3 Announce Type: replace-cross Abstract: We present eCREAM-MedCorpus, a new and unique large-scale dataset of clinical notes produced in Emergency Departments of Italian hospitals.
By Tiziano Labruna, Guido Bertolini, Pietro Ferrazzi, Bernardo Magnini
arXiv:2608.29890v1 Announce Type: new
Abstract: Biomedical Named Entity Recognition (NER) is fundamental to healthcare AI applications, including clinical decision support and medical information ext...
By Nhu Vo, Phuong Nguyen, Nu Uyen Phuong Le, Inigo Jauregi Unanue, Dung D. Le, Massimo Piccardi, Wray Buntine
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.
By Valentina Bui Muti, Eug\'enie Dulout, Ziquan Fu
The study investigates whether cross‑lingual clinical annotation can be treated as a constrained text‑generation task that preserves the original text while inserting entity tags. Using a workflow that embeds tags directly into immutable target‑language text and then validates them deterministically, the authors compare this approach to supervised candidate‑span projection and hybrid ML‑LLM refinement across six languages. Results show that direct LLM projection, particularly with GLM 5.2 and Gemma4:31B, achieves the highest strict F1 scores, surpassing previous state‑of‑the‑art by up to 0.15 and producing over 55,000 grounded mentions with accurate offsets.
The paper presents a method for extracting key information from OCR‑digitized clinical reports, addressing challenges posed by heterogeneous documents and noisy OCR output. It introduces an open key space that is iteratively mined, normalized, clustered, and verified to build a canonical key inventory, and defines key coverage as a metric for inventory completeness. Experiments on reports from over 20 hospitals using a 0.2B BERT model show that performance improves steadily with key coverage, achieving high F1 scores when the top 90 keys are covered and outperforming a fine‑tuned Qwen3‑0.6B baseline.
By Yu Wang, Yingyun Li, Ying Qin, Haiyang Qian
MedStruct‑S is a benchmark for semi‑structured information extraction from OCR‑derived clinical reports, covering key discovery, key‑conditioned QA, and end‑to‑end key‑value extraction. It contains 3,582 annotated real‑world report pages and evaluates models under unknown keys and OCR noise. Experiments show encoder‑only models excel at non‑null key‑conditioned QA, while fine‑tuned decoder‑only models achieve the strongest overall performance across model sizes.
By Yingyun Li, Yu Wang, Haiyang Qian