Researchers proposed HERMES, a graph-based framework that predicts patient outcomes using only clinical text, building personalized knowledge graphs via Large-Language-Model-guided extraction with Con…
Researchers propose REFINE, a KG-aware budgeted LLM graph refinement framework that learns patient-personalized medical concept representations from text-attributed knowledge graphs, using a sequentia…
Researchers propose ReTA, a reinforcement learning-based dynamic topology augmentation framework that selectively imports external knowledge graph nodes and edges into electronic health record (EHR) v…
Researchers propose CDPR (Counterfactual Diagnostic Process Reward), a reinforcement learning method for cost-aware sequential medical diagnosis that assigns credit by comparing chosen actions against…
Researchers introduced the Relational Hypergraph Transformer (RHT), a unified architecture for multi-table learning that represents relational databases as hypergraphs and uses pentadimensional embedd…
Researchers released MTDiag, a multi-turn diagnostic dialogue dataset built from DDXPlus, MIMIC-IV, and published case reports, to evaluate large language models (LLMs) as diagnostic agents in dynamic…
MedEventGraph-RAG, an evidence-admissible framework for longitudinal clinical event verification, achieves balanced accuracies of 78.6, 67.3, and 96.8 on temporal, medication-adverse-event, and record…
Researchers Minjun Kim and Jong Hak Moon proposed Mr.Dec, a Transformer Decoder-based model that predicts 30-day hospital readmission by representing each admission as a time-aligned sequence of daily…
A JAMA viewpoint argues that autonomous AI will outperform every doctor-AI team, citing automation bias and human latency as the weak link once model performance exceeds a threshold. The authors conce…
Researchers introduced ICD-Deepresearch, a DeepResearch workflow that combines structured EHR foundation models with language foundation models to forecast ICD codes for future clinical visits, achiev…
Researchers propose Mr.Dec, a multimodal Transformer Decoder that models hospital admissions as daily sequences of EHR updates and chest X-ray findings, achieving state-of-the-art performance on MIMIC…
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…