arXiv:2606. 26205v1 Announce Type: new Abstract: Patients increasingly seek medication information online, yet safety knowledge for psychiatric drugs is split between regulatory adverse-event records, which are authoritative but abstract, and patient narratives, which are experience-near but unvalidated.
arXiv:2608. 09443v1 Announce Type: new Abstract: Large language model (LLM) agents can support medication review between clinical visits, but safe choices for older adults with multimorbidity depend on conditions, medications, and geriatric risks that users may omit.
arXiv:2603. 01131v3 Announce Type: replace-cross Abstract: Clinical diagnosis is a gradual process of evidence integration, in which physicians move from symptoms and medical history to examinations, competing hypotheses, disease relations, and treatment decisions.
arXiv:2606. 28692v1 Announce Type: new Abstract: Treatment reasoning underpins every therapeutic decision, integrating disease context, comorbidities, medications, contraindications, and evolving biomedical knowledge to select an appropriate therapy.
The paper introduces MIMIC-DOS, a dataset derived from MIMIC-IV that focuses on ICU cases where patient symptoms and medical signs are discordant. It presents CARE, a privacy‑compliant multi‑stage agentic reasoning framework that uses a proprietary LLM to generate structured categories and transitions, while a local LLM performs evidence acquisition and decision‑making. In retrospective evaluations on MIMIC‑DOS, CARE outperforms other LLMs and agentic workflows, demonstrating stronger handling of conflicting clinical evidence while preserving patient privacy.
arXiv:2606. 19602v1 Announce Type: new Abstract: Patient contexts span hundreds of heterogeneous documents and thousands of structured data points, yet the document-level metadata that AI systems need for retrieval and triage is absent or incomplete.
arXiv:2606. 31085v1 Announce Type: new Abstract: Drug-drug interaction (DDI) prediction is essential for medication safety, yet it requires reasoning over heterogeneous biomedical evidence whose relevance changes across interaction mechanisms.
arXiv:2606. 14149v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in healthcare settings, yet their tendency to hallucinate poses risks when clinical decisions are involved.
EHR2Trace is a system that transforms electronic health records from multiple sources into a standardized, traceable event format suitable for training and evaluating patient world models and clinical agents. It links each event to its original record, separates the event time from the time the information became available, and distinguishes between medication orders, dispensing, and administration. The tool supports both OMOP and MEDS data models, includes automated validation, and was tested on three clinical datasets, converting 846.4 million events and detecting all injected faults.
The paper introduces Clinical Graph-JEPA, a framework for building and refining predictive patient-state knowledge graphs from clinical records. It combines multi-agent relation proposal, ontology-aware normalization, deterministic evidence scoring, and JEPA-based latent refinement to construct evidence-scored graphs from MIMIC-IV data and recover missing clinical relations. Experiments show that injecting discharge-note representations into note-grounded entities boosts leave-one-out MRR by 31% relative improvement.
arXiv:2608.30393v1 Announce Type: new Abstract: Biomedical artificial intelligence (AI) systems increasingly extract, organize, and reuse scientific claims from literature, clinical trials, and regul...
arXiv:2608. 07796v1 Announce Type: new Abstract: Large language models perform strongly on medical knowledge benchmarks, but reliable clinical deployment requires agents to conduct defensible investigations over heterogeneous, longitudinal records: determining what evidence is needed, retrieving and reconciling structured and free-text data, grounding conclusions in verifiable evidence, and deferring cases that cannot be resolved reliably.
The article proposes a framework called quantitative evidence mining to transform biomedical findings into structured, context-rich evidence units. It outlines core elements such as claim, measured entity, value, comparator, population, conditions, temporal context, uncertainty, provenance, validation, and expert review. The authors present an eight-stage reference architecture and emphasize that plausibility should remain multidimensional rather than collapsed into a single truth label, linking extraction to evidence synthesis for applications like clinical trials, biomarker research, and knowledge-graph construction.