A new expert-guided neuro-symbolic pipeline that combines a large language model with a Sugeno fuzzy inference system to assess compliance with sepsis treatment protocols found that antibiotic timing …
CoMedBench, a new benchmark introduced in arXiv:2608.12805v1, evaluates synthetic medical data fidelity and downstream utility across 37 dataset-task pairs from seven public sources, including MIMIC-I…
Researchers propose MiGHT-EHR, a multi-task graph transformer for heterogeneous temporal electronic health records, which jointly models heterogeneous clinical entities, longitudinal patient trajector…
Researchers introduced EarlyDx, a benchmark built from 154,834 emergency department encounters in MIMIC-IV, to evaluate open-ended early diagnosis at hospital admission. The study found that no evalua…
Researchers released MedLoCoMo, a medical long-context benchmark for evaluating large language models on patient-specific clinical reasoning over multi-admission dialogues, built from deidentified MIM…
A new multi-agent framework called GraphDx improves diagnostic success rates from 50-68% to 79-93% while reducing test costs by 20-54%, according to a preprint on arXiv (2607.15280v1). The knowledge-e…
Graph-constrained AI models outperform traditional methods in clinical code prediction, achieving a 0.709 micro-F1 score on a curated 50-code subset and 0.527 on the full 15,761-code space of the MIMI…
LongMedBench, a new benchmark for long-term clinical decision-making using real-world EHR data from 335 patients with an average of 19.72 inpatient visits and 44.91 medical events per visit, reveals t…
Researchers introduced LongMedBench, a benchmark for evaluating LLM-based medical agents on long-horizon clinical decision-making using real-world EHR data from MIMIC-IV. The benchmark includes 335 pa…
Weill Cornell Medicine researchers published EmulatRx in Nature Communications on July 7, 2026, a five-agent LLM framework for clinical-trial design using real-world patient data from EHR datasets suc…
Researchers used frozen medical large language model (LLM) representations to predict primary ICD categories from both clinical narratives and structured EHR data, achieving 87.69% strict accuracy on …