The LLENS platform is a clinical support AI system deployed inside Hokkaido University Hospital’s closed network, running the open‑weight model Kimi K2.6 on a single host with eight NVIDIA H200 GPUs. It includes ten harness components—such as page image reading, full‑text attachment input, a sandbox, and self‑checking—allowing the model to read and interact with hospital documents. In evaluation, the system achieved 97.2 % accuracy on 180 document‑based questions when using interactive access, outperforming fixed retrieval by 15.9 percentage points, and has been operational since September 14, 2026.
arXiv:2608. 12138v1 Announce Type: cross Abstract: General-purpose large language models (LLMs) have recently been reported to match or exceed specialized clinical AI tools on medical benchmarks, but such comparisons draw on a narrow set of systems and on benchmarks developed largely in high-income settings.
The paper demonstrates that large language models (LLMs) used to preliminarily assess medical device documentation can produce inconsistent results across different commercial deployments and settings. By constructing a fully deterministic pipeline—including OCR, chunking, embedding, indexing, retrieval, and inference—the authors achieved byte‑identical outputs across repeated runs, varied machines, and even interruptions. The study shows that end‑to‑end determinism is possible and provides a verifiable foundation for audit and change control in regulatory compliance.
arXiv:2509. 02594v3 Announce Type: replace-cross Abstract: Evaluating large language models (LLMs) on their ability to generate high-quality, accurate, situationally aware answers to clinical questions requires going beyond conventional benchmarks to assess how these systems behave in complex, high-stakes clinical scenarios.
MedPAO-Assist is an edge‑deployable, agentic AI framework that structures radiology reports from radiologist dictation and voice commands using only about 3.3 GB of RAM and an average latency of 31 seconds on a standard laptop CPU. It supports four imaging modalities—Chest X‑ray, Chest CT, Abdomen CT, and Brain MRI—and operates entirely with a 0.6 B‑parameter small language model, avoiding cloud APIs or high‑end GPUs. The system follows an Extract‑Ground‑Generate pipeline, performing concept extraction, SNOMED‑CT grounding, structured report generation, and a speech‑driven human‑in‑the‑loop correction phase, enabling a fully “no‑touch” reporting workflow that requires no keyboard input or internet connectivity.
arXiv:2610.23480v1 Announce Type: new Abstract: Research groups that want a language model grounded in their own literature face a choice between hosted services that receive their unpublished work a...
arXiv:2606. 01961v1 Announce Type: new Abstract: Autonomous agents are increasingly expected to support end-to-end medical-AI research workflows, moving beyond isolated prediction tasks or short-form clinical question answering.
The paper introduces Clinical System One (CS1), a 4‑billion‑parameter decision model that rapidly produces typed judgments from clinical records. Trained on 12.4 million Q&A pairs from 3.6 million patients, CS1 can answer multiple questions in a single forward pass, providing probability estimates for uncertain cases. In evaluations on 963,408 held‑out patients and 21 external datasets, CS1 outperformed larger open models and, when paired with a language‑model agent, efficiently identified trial‑eligible patients with far fewer tokens.
Large language model assistants are being deployed to clinicians, but their readiness is judged mainly by benchmark scores that are based on examination questions or curated cases. This study analyzed 127,833 queries from 6,342 healthcare professionals across 35 specialties, finding that most requests were for documentation, administration, and knowledge retrieval, with a significant portion of queries unanswerable. When compared to 58 public benchmarks, the median benchmark lacked documentation requests and only shared 31% of the real task mix, indicating that current benchmarks poorly reflect actual clinical use.
The paper introduces a multi‑agent large language model framework that interprets personalized health checkup results by assigning distinct tasks to specialized agents and combining their outputs. Experiments on 120 Korean compound queries show that the multi‑agent approach improves overall quality scores and user preference compared to a single‑agent baseline, though it incurs higher latency and cost. The benefits are most pronounced for queries requiring personal‑record lookup.
arXiv:2610.14428v1 Announce Type: new Abstract: Enterprise workflow automation typically requires retrieving a complete and accurate set of relevant information from large operational document corpor...
arXiv:2607. 25485v1 Announce Type: new Abstract: Health AI is evolving from answering questions to agentic systems that converse with patients, reason about health records, and act on their behalf.
arXiv:2606. 03157v1 Announce Type: new Abstract: Large language models (LLMs) have been widely adopted in healthcare, yet they still encounter significant challenges in complex clinical decision-making scenarios.