A Locally Executable AI System for Improving Preoperative Patient Communication: A Multi-Domain Clinical Evaluation

• URL: http://arxiv.org/abs/2510.01671v1
• Date: Thu, 02 Oct 2025 04:53:11 GMT
• Title: A Locally Executable AI System for Improving Preoperative Patient Communication: A Multi-Domain Clinical Evaluation
• Abstract summary: LENOHA is a safety-first, local-first system that routes inputs with a high-precision sentence-transformer constraints.<n>It returns verbatim answers from a clinician-curated FAQ for clinical queries.<n>Energy logging shows that the non-generative clinical path consumes 1.0 mWh per input versus 168 mWh per small-talk reply.
• Score: 1.9205944025326396
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Patients awaiting invasive procedures often have unanswered pre-procedural questions; however, time-pressured workflows and privacy constraints limit personalized counseling. We present LENOHA (Low Energy, No Hallucination, Leave No One Behind Architecture), a safety-first, local-first system that routes inputs with a high-precision sentence-transformer classifier and returns verbatim answers from a clinician-curated FAQ for clinical queries, eliminating free-text generation in the clinical path. We evaluated two domains (tooth extraction and gastroscopy) using expert-reviewed validation sets (n=400/domain) for thresholding and independent test sets (n=200/domain). Among the four encoders, E5-large-instruct (560M) achieved an overall accuracy of 0.983 (95% CI 0.964-0.991), AUC 0.996, and seven total errors, which were statistically indistinguishable from GPT-4o on this task; Gemini made no errors on this test set. Energy logging shows that the non-generative clinical path consumes ~1.0 mWh per input versus ~168 mWh per small-talk reply from a local 8B SLM, a ~170x difference, while maintaining ~0.10 s latency on a single on-prem GPU. These results indicate that near-frontier discrimination and generation-induced errors are structurally avoided in the clinical path by returning vetted FAQ answers verbatim, supporting privacy, sustainability, and equitable deployment in bandwidth-limited environments.

Related papers

• Post-Operative Glioma Segmentation via Loss Stabilization, Normalization and Subspace Attention [46.03321798937855]
  We show that the standard Generalized Dice Loss (GDL) is unstable under domain shift.<n>We propose a Subspace-Aware Class Attention (SACA) module that re-calibrates the bottleneck features and raises Enhancing Tumor (ET) sensitivity by 8%.
  arXiv  Detail & Related papers  (2026-07-23T09:59:30Z)
• Enhancing Clinician Decision-Making via Uncertainty-Aware Multi-Expert Fusion for Stroke Rehabilitation [0.32553561239735207]
  We present xAARA: an engine designed to augment rather than replace clinical judgment.<n>xAARA returns ARAT assessments with calibrated uncertainty and explanations across task, movement-phase, and movement-quality levels.<n>In 105 stroke survivors (788 exercises), xAARA achieved 94.2% task accuracy and 81.3% movement-phase accuracy.
  arXiv  Detail & Related papers  (2026-06-23T09:00:15Z)
• ADAPTS: Agentic Decomposition for Automated Protocol-agnostic Tracking of Symptoms [36.94429692322632]
  ADAPTS (Agentic Decomposition for Automated Protocol-agnostic Tracking of Symptoms) is a framework for automated rating of depression and anxiety severity.<n>This approach decomposes long-form clinical interviews into symptom-specific reasoning tasks.<n>Ratings approximated expert benchmarks more closely than original human ratings.
  arXiv  Detail & Related papers  (2026-05-04T23:08:42Z)
• Detecting Clinical Discrepancies in Health Coaching Agents: A Dual-Stream Memory and Reconciliation Architecture [71.46525715889656]
  General-purpose agent memory systems optimize for coherence by overwriting older facts with the user's latest statement.<n>We introduce a Dual-Stream Memory Architecture that strictly separates the patient narrative from the structured clinical record.<n>We evaluate this architecture on 26 patients across 675 longitudinal wellness coaching sessions.
  arXiv  Detail & Related papers  (2026-04-29T17:59:28Z)
• From Black Box to Glass Box: Cross-Model ASR Disagreement to Prioto Review in Ambient AI Scribe Documentation [43.148402136307716]
  Cross-model disagreement among heterogeneous ASR systems can act as a reference-free uncertainty signal.<n>We transcribed 50 publicly available medical audio clips with eight ASR systems spanning commercial APIs and open-source engines.<n>Low-agreement regions were enriched for content disagreements, with the content fraction increasing from 53.9% to 73.9% across quintiles of high-risk mass.
  arXiv  Detail & Related papers  (2026-03-02T13:02:13Z)
• Automated Rubrics for Reliable Evaluation of Medical Dialogue Systems [19.880569341968023]
  Large Language Models (LLMs) are increasingly used for clinical decision support, where hallucinations and unsafe suggestions may pose direct risks to patient safety.<n>We propose a retrieval-augmented multi-agent framework designed to automate the generation of instance-specific evaluation rubrics.
  arXiv  Detail & Related papers  (2026-01-21T16:40:41Z)
• CPGPrompt: Translating Clinical Guidelines into LLM-Executable Decision Support [18.887576751340884]
  We develop and validate CPGPrompt, an auto-prompting system that converts narrative clinical guidelines into large language models (LLMs)<n>Our framework translates CPGs into structured decision trees and utilizes an LLM to dynamically navigate them for patient case evaluation.<n>System performance was assessed on both binary specialty-referral decisions and fine-grained pathway-classification tasks.
  arXiv  Detail & Related papers  (2026-01-07T00:05:42Z)
• A DeepSeek-Powered AI System for Automated Chest Radiograph Interpretation in Clinical Practice [83.11942224668127]
  Janus-Pro-CXR (1B) is a chest X-ray interpretation system based on DeepSeek Janus-Pro model.<n>Our system outperforms state-of-the-art X-ray report generation models in automated report generation.
  arXiv  Detail & Related papers  (2025-12-23T13:26:13Z)
• Retrieval-Augmented Guardrails for AI-Drafted Patient-Portal Messages: Error Taxonomy Construction and Large-Scale Evaluation [5.555479009357263]
  Asynchronous patient-clinician messaging via EHR portals is a growing source of clinician workload.<n>Our contributions are threefold: (1) we introduce a clinically grounded error ontology comprising 5 domains and 59 granular error codes; (2) we develop a retrieval-augmented evaluation pipeline; and (3) we provide a two-stage prompting architecture using DSPy to enable scalable, interpretable, and hierarchical error detection.
  arXiv  Detail & Related papers  (2025-09-26T16:42:43Z)
• EchoBench: Benchmarking Sycophancy in Medical Large Vision-Language Models [82.43729208063468]
  Recent benchmarks for medical Large Vision-Language Models (LVLMs) emphasize leaderboard accuracy, overlooking reliability and safety.<n>We study sycophancy -- models' tendency to uncritically echo user-provided information.<n>We introduce EchoBench, a benchmark to systematically evaluate sycophancy in medical LVLMs.
  arXiv  Detail & Related papers  (2025-09-24T14:09:55Z)
• Beyond Benchmarks: Dynamic, Automatic And Systematic Red-Teaming Agents For Trustworthy Medical Language Models [87.66870367661342]
  Large language models (LLMs) are used in AI applications in healthcare.<n>Red-teaming framework that continuously stress-test LLMs can reveal significant weaknesses in four safety-critical domains.<n>A suite of adversarial agents is applied to autonomously mutate test cases, identify/evolve unsafe-triggering strategies, and evaluate responses.<n>Our framework delivers an evolvable, scalable, and reliable safeguard for the next generation of medical AI.
  arXiv  Detail & Related papers  (2025-07-30T08:44:22Z)
• Neural at ArchEHR-QA 2025: Agentic Prompt Optimization for Evidence-Grounded Clinical Question Answering [3.3260862557368926]
  We present Neural, the runner-up in the BioNLP 2025 Arch-QA shared task on evidence-grounded clinical QA.<n>Our proposed method decouples the task into (1) sentence-level evidence identification and (2) answer synthesis with explicit citations.<n>A self-consistency voting scheme further improves evidence recall without sacrificing precision.
  arXiv  Detail & Related papers  (2025-06-12T14:36:18Z)
• Uncertainty-guided annotation enhances segmentation with the human-in-the-loop [5.669636524329784]
  Uncertainty-Guided. (UGA) introduces a human-in-the-loop approach, enabling AI to convey its uncertainties to clinicians. UGA eases this interaction by quantifying uncertainty at the pixel level, thereby revealing the model's limitations. To foster broader application and community contribution, we have made our code accessible.
  arXiv  Detail & Related papers  (2024-02-16T16:41:15Z)
• Zero-Shot Clinical Trial Patient Matching with LLMs [40.31971412825736]
  Large language models (LLMs) offer a promising solution to automated screening. We design an LLM-based system which, given a patient's medical history as unstructured clinical text, evaluates whether that patient meets a set of inclusion criteria. Our system achieves state-of-the-art scores on the n2c2 2018 cohort selection benchmark.
  arXiv  Detail & Related papers  (2024-02-05T00:06:08Z)
• Exploiting prompt learning with pre-trained language models for Alzheimer's Disease detection [70.86672569101536]
  Early diagnosis of Alzheimer's disease (AD) is crucial in facilitating preventive care and to delay further progression. This paper investigates the use of prompt-based fine-tuning of PLMs that consistently uses AD classification errors as the training objective function.
  arXiv  Detail & Related papers  (2022-10-29T09:18:41Z)
• An End-to-End Set Transformer for User-Level Classification of Depression and Gambling Disorder [24.776445591293186]
  This work proposes a transformer architecture for user-level classification of gambling addiction and depression. We process a set of social media posts from a particular individual, to make use of the interactions between posts and eliminate label noise at the post level. Our architecture is interpretable with modern feature attribution methods and allows for automatic dataset creation.
  arXiv  Detail & Related papers  (2022-07-02T06:40:56Z)
• Controlling False Positive/Negative Rates for Deep-Learning-Based Prostate Cancer Detection on Multiparametric MR images [58.85481248101611]
  We propose a novel PCa detection network that incorporates a lesion-level cost-sensitive loss and an additional slice-level loss based on a lesion-to-slice mapping function. Our experiments based on 290 clinical patients concludes that 1) The lesion-level FNR was effectively reduced from 0.19 to 0.10 and the lesion-level FPR was reduced from 1.03 to 0.66 by changing the lesion-level cost.
  arXiv  Detail & Related papers  (2021-06-04T09:51:27Z)
• DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentation [0.3734402152170273]
  We propose a novel architecture called DDANet'' based on a dual decoder attention network. Experiments demonstrate that the model trained on the Kvasir-SEG dataset and tested on an unseen dataset achieves a dice coefficient of 0.7874, mIoU of 0.7010, recall of 0.7987, and a precision of 0.8577.
  arXiv  Detail & Related papers  (2020-12-30T17:52:35Z)
• Collaborative residual learners for automatic icd10 prediction using prescribed medications [45.82374977939355]
  We propose a novel collaborative residual learning based model to automatically predict ICD10 codes employing only prescriptions data. We obtain multi-label classification accuracy of 0.71 and 0.57 of average precision, 0.57 and 0.38 of F1-score and 0.73 and 0.44 of accuracy in predicting principal diagnosis for inpatient and outpatient datasets respectively.
  arXiv  Detail & Related papers  (2020-12-16T07:07:27Z)
• Ensemble model for pre-discharge icd10 coding prediction [45.82374977939355]
  We propose an ensemble model incorporating multiple clinical data sources for accurate code predictions. We obtain multi-label classification accuracies of 0.73 and 0.58 for average precision, 0.56 and 0.35 for F1-scores and 0.71 and 0.4 accuracy in predicting principal diagnosis for inpatient and outpatient datasets respectively.
  arXiv  Detail & Related papers  (2020-12-16T07:02:56Z)
• Noisy Adaptive Group Testing using Bayesian Sequential Experimental Design [63.48989885374238]
  When the infection prevalence of a disease is low, Dorfman showed 80 years ago that testing groups of people can prove more efficient than testing people individually. Our goal in this paper is to propose new group testing algorithms that can operate in a noisy setting.
  arXiv  Detail & Related papers  (2020-04-26T23:41:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.