3arXiv cs.AI (Artificial Intelligence)·
FMRP-LEAN: HIPAA-compliant AI-augmented LIMS architecture for clinical assay workflow management
A preprint from arXiv introduces FMRP-LEAN, a Laboratory Information Management System (LIMS) architecture designed for HIPAA-compliant clinical biomarker workflows in translational research. The system combines a self-hosted Supabase/PostgreSQL stack, finite-state workflow modeling, REDCap synchronization, and a governance-constrained AI operations module that operates only on aggregate projections with deterministic fallback guarantees. The architecture targets multi-day assays like Luminex-based FMRP quantification, demonstrating improved workflow observability and reduced QC latency. It represents a deployment case study of constrained AI integration in regulated healthcare infrastructure.
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RRBench: evaluating open-weight LLMs on longitudinal data preparation tasks in governance-restricted settings
Researchers from UCL introduce RRBench, an open-source benchmark and evaluation framework for assessing open-weight LLMs on data preparation tasks in longitudinal population studies, where cloud-based models are prohibited by data governance rules. The benchmark uses ground-truth cleaning scripts from six sweeps of a British cohort study, covering tasks like category harmonization and multi-wave merging, with automated evaluation of LLM-generated R code. Current 31–35B parameter open-weight models achieve up to 87.9% average task completion on consumer-grade hardware, suggesting a viable path for AI-assisted data preparation in privacy-sensitive research contexts.
Large Cancer Assistant (LCA): Model-agnostic orchestration framework for oncology clinical decision support
Researchers propose the Large Cancer Assistant (LCA), a model-agnostic orchestration framework for multimodal AI in oncology that decouples data ingestion, clinical routing, and AI inference via a 7-tuple architecture grounded in 'Algorithmic Impermeability.' The system uses Geometric Deep Learning to standardize multimodal patient data and outputs a Standardized Intermediate Payload (SIP) to isolate AI execution from hospital IT infrastructure. A proof-of-concept validated orchestration logic across four scenarios, demonstrating invariant routing during model swaps and 100% recall on failure-safety under injected data anomalies. The framework targets EMR interoperability and modular deployment of heterogeneous oncology AI models.
MedRLM: Recursive multimodal agent framework for long-context clinical decision support
MedRLM is a proposed framework for clinical decision support that uses recursive multi-agent reasoning over heterogeneous patient data including EHRs, medical images, physiological sensor streams, and clinical guidelines. Rather than single-step prompting, it decomposes patient cases into an inspectable external environment coordinated by specialized agents, with a Clinical Evidence Graph Memory and sensor-triggered deeper reasoning. The paper outlines an evaluation design using public and credentialed clinical datasets spanning radiology, ECG, ICU time series, and referral outcomes. The work targets a gap between static medical QA benchmarks and real-world longitudinal clinical workflows.
ClaMPAPP: Hybrid LLM-ML system uses language models as interfaces for pediatric appendicitis diagnosis
Researchers introduce ClaMPAPP, a hybrid clinical decision support system that uses an LLM solely for structured feature extraction from free-text clinical notes, then passes validated features to an XGBoost classifier for final diagnosis. Evaluated on two independent German pediatric appendicitis cohorts, ClaMPAPP outperformed end-to-end LLM baselines on diagnostic performance and showed greater robustness to narrative reordering. The work formalizes an 'LLM-as-interface, ML-as-predictor' design pattern that separates natural-language usability from predictive inference, offering a more auditable pathway for clinical AI.
ClinEnv: Interactive Multi-Stage Long-Horizon EHR Benchmark for Clinical Agent Evaluation
ClinEnv is a new interactive benchmark that evaluates LLMs as attending physicians over real inpatient admissions using a Longitudinal Inpatient Simulation paradigm. Each case is decomposed into sequential decision stages where models must query four specialized agents before committing to medications, procedures, and diagnoses. Across seven evaluated models, the best achieves only 0.31 decision F1, with a sharp gap between diagnosis recovery (0.51 F1) and management actions (0.17 F1). The benchmark uniquely measures information-acquisition process quality alongside outcome quality, exposing a gap invisible to static or outcome-only evaluations.
MedFailBench: Clinician-built benchmark for medical AI safety boundary failures
MedFailBench is a new open-source benchmark designed to identify which safety boundary a medical AI system failed, rather than measuring correctness. The v0.2.1 release contains 44 clinician-reviewed synthetic cases annotated by severity (1–5) and six safety gate types including missed urgent escalation, unsafe dosing, and evidence fabrication. The benchmark includes a HuggingFace leaderboard preview, a safety gate taxonomy, and an automated pipeline for archiving model-response screening runs, released under Apache-2.0 and CC-BY-4.0.
MOSAIC: Multi-LLM agentic framework for clinical severity phenotyping in EHR data
MOSAIC is a two-phase agentic LLM framework for disease severity phenotyping applied to type 2 diabetes, evaluated on a synthetic EHR cohort of up to 4,886 patients. The system incorporates domains absent from traditional algorithmic comparators—including glycaemic staging, beta-cell function, and social determinants of health—and shows open-weight models matching proprietary pipelines (weighted kappa 0.773). Agentic classification diverged meaningfully from deterministic rule execution of the same rubric (kappa 0.428), suggesting genuine reasoning beyond fixed rules. The work provides early evidence that agentic LLM systems can generate clinically meaningful severity phenotypes from structured EHR data.
MedCase-Structured: A Text-to-FHIR Dataset for Benchmarking Diagnostic Reasoning in Clinically Realistic EHR Settings
The paper introduces a pipeline for converting unstructured clinical text into HL7 FHIR R4 bundles, enabling evaluation of LLMs in realistic electronic health record settings. Applied to the MedCaseReasoning dataset, it produces MedCase-Structured, a synthetic benchmark achieving valid FHIR generation for 82.5% of cases. Key finding: LLMs show consistently lower diagnostic accuracy on structured FHIR inputs compared to plain text, underscoring the gap between standard benchmarks and real-world clinical deployment conditions.