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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:2607. 28788v1 Announce Type: new Abstract: Clinical diagnosis at hospital admission must be made rapidly from limited, incomplete evidence.
arXiv:2605. 03301v2 Announce Type: replace-cross Abstract: De-identification of clinical text is a prerequisite for the secondary use of electronic health records.
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.
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.
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.