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最近一次命中来自 LLM：Four-Axis Decision Alignment for Long-Horizon Enterprise AI Agents
2026-04-09
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命中明细
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2026-04-22
Four-Axis Decision Alignment for Long-Horizon Enterprise AI Agents
Long-horizon enterprise agents make high-stakes decisions (loan underwriting, claims adjudication, clinical review, prior authorization) under lossy memory, multi-step reasoning,…
RF-HiT: Rectified Flow Hierarchical Transformer for General Medical Image Segmentation
Accurate medical image segmentation requires both long-range contextual reasoning and precise boundary delineation, a task where existing transformer- and diffusion-based paradigm…
Classifying American Society of Anesthesiologists Physical Status With a Low-Rank-Adapted Large Language Model: Development and Validation Study.
BACKGROUND: The American Society of Anesthesiologists Physical Status (ASA-PS) classification is integral to preoperative risk assessment; yet, assignment remains subjective and l…
Enhancing large language model clinical support information with machine learning risk and explainability: a feasibility study.
BACKGROUND: Current machine learning (ML) prediction models offer limited guidance for individualized actionable management. Large language models (LLMs) can transform ML model-pr…
Clinical Model Autophagy: The Risk of Interpretative Drift in Recursive Medical AI.
The rapid integration of large language models into electronic medical record systems introduces a critical theoretical vulnerability. Drawing on foundational computer science pro…
APSevLM: Acute Pancreatitis Severity Language Model.
Approximately one-fifth of patients with acute pancreatitis (AP) develop severe forms, which are associated with high mortality rates, making early prediction of severity crucial…
Comparing Clinical Outcomes in Cardiac Surgical Patients Who Receive Sugammadex Versus Placebo: A Prospective Randomized Blinded Controlled Trial.
OBJECTIVES: To compare the difference in the number of cardiopulmonary bypass surgical patients who receive sugammadex vs. placebo and who meet the Society of Thoracic Surgery ear…
2026-04-21
Transforming oncology clinical trial matching through neuro-symbolic, multi-agent AI and an oncology-specific knowledge graph: a prospective evaluation in 3804 patients.
BACKGROUND: Clinical trial enrollment in oncology remains critically low, with fewer than 5% of eligible adults participating, in large part due to the complexity and labor intens…
Developing and evaluating definitions of real-world clinical endpoints for patients with early-stage triple-negative breast cancer using a United States of America secondary database.
BACKGROUND: The KEYNOTE-522 trial showed that neoadjuvant chemotherapy (NAC) plus adjuvant pembrolizumab improved overall survival, event-free survival (EFS), and pathological com…
Investigating fine-tuning versus zero-shot learning for general large language models when predicting cancer survival from initial oncology consultation documents.
BACKGROUND: Unstructured oncology consultation notes contain rich clinical information that may support survival prediction. Open-weight large language models (LLMs) can utilize t…
A Comparative Evaluation of Three Large Language Models for Parent-Centered Questions About Anorexia Nervosa.
BACKGROUND: Large language models (LLMs) are increasingly used to obtain health information, including guidance on child and adolescent mental health. In anorexia nervosa (AN), wh…
Impacts of Multidisciplinary Lung Cancer Meeting Presentation in a Clinical Quality Registry.
BACKGROUND: Lung cancer is a heterogeneous and complex disease requiring multidisciplinary input for optimal management planning, with guidelines recommending that all patients be…
2026-04-20
Medic Training at Military-Civilian Partnerships-A Narrative Review.
INTRODUCTION: Military-Civilian Partnerships (MCP) were developed to mitigate degradation of combat medical readiness during peacetime. Although these programs have historically f…
2026-04-18
Pretraining effective T5 generative models for clinical and biomedical applications.
This paper presents a study of the impact of corpus selection and vocabulary design on the performance of T5-based language models in clinical and biomedical domains. We introduce…
Comparative performance of large language models and Drugs.com versus Lexicomp for antiseizure medication drug-drug interactions: A cross-sectional study with iterative prompting analysis.
BACKGROUND: Antiseizure medications (ASMs) are frequently co-prescribed and are associated with a high risk of clinically significant drug-drug interactions (DDIs). Large language…
An explainable multi-head attention network for healthcare IoT threat detection based on the MedDefender-MHAN framework.
The rapid proliferation of Internet of Medical Things (IoMT) devices in healthcare environments has created critical cybersecurity vulnerabilities that demand both accurate and in…
2026-04-17
RadAgent: A tool-using AI agent for stepwise interpretation of chest computed tomography
Vision-language models (VLM) have markedly advanced AI-driven interpretation and reporting of complex medical imaging, such as computed tomography (CT). Yet, existing methods larg…
SegWithU: Uncertainty as Perturbation Energy for Single-Forward-Pass Risk-Aware Medical Image Segmentation
Reliable uncertainty estimation is critical for medical image segmentation, where automated contours feed downstream quantification and clinical decision support. Many strong unce…
Applying natural language processing and large language models to clinical notes for phenotyping and diagnosing rare diseases: a systematic review.
OBJECTIVES: Patients with rare diseases often face long delays before receiving a diagnosis. Using electronic health records for automated phenotyping and diagnosis of rare diseas…
Evaluation of large language models with clinical guidance for vetting outpatient magnetic resonance imaging lumbar spine referrals.
ObjectivesAccurate triage of lumbar spine magnetic resonance imaging (MRI) referrals for sciatica is important for patient assessment, diagnosis and surgical planning. This study…
From Image to Pixels: towards Fine-Grained Medical Vision-Language Models.
Multimodal large language models (MLLMs) offer immense potential for biomedical AI, yet current applications remain limited to coarse-grained image understanding and basic textual…
Targeted use of large language models for EHR-based computable phenotyping.
OBJECTIVE: Computable phenotypes derived from electronic health records (EHRs) are central to clinical research and quality reporting. Although large language models (LLMs) can ex…
Dual perspectives on large language models in rheumatology: physician-rated quality and patient-centered usability of GPT-4o versus DeepSeek-V3.
OBJECTIVES: This study conducted an informatics system evaluation of two LLMs (GPT-4o and DeepSeek-V3) for patient education, combining clinician-rated quality with patient-percei…
2026-04-16
MedRCube: A Multidimensional Framework for Fine-Grained and In-Depth Evaluation of MLLMs in Medical Imaging
The potential of Multimodal Large Language Models (MLLMs) in domain of medical imaging raise the demands of systematic and rigorous evaluation frameworks that are aligned with the…
PBE-UNet: A light weight Progressive Boundary-Enhanced U-Net with Scale-Aware Aggregation for Ultrasound Image Segmentation
Accurate lesion segmentation in ultrasound images is essential for preventive screening and clinical diagnosis, yet remains challenging due to low contrast, blurry boundaries, and…
Augmenting Large Language Model With Prompt Engineering and Supervised Fine-Tuning in Non-Small Cell Lung Cancer Tumor-Node-Metastasis Staging: Framework Development and Validation.
BACKGROUND: Accurate tumor node metastasis (TNM) staging is fundamental for treatment planning and prognosis in non-small cell lung cancer (NSCLC). However, its complexity poses s…
PKFAR: psychiatry knowledge-fused augmented reasoning with large language models.
PURPOSE: Psychiatric diagnosis faces significant challenges due to subjective symptom reporting and complex diagnostic criteria. While Large Language Models (LLMs) offer potential…
Fact-Checking Large Language Model Responses to a Health Care Prompt: Comparative Study.
BACKGROUND: Large language models use machine learning to produce natural language. These models have a range of potential applications in health care, such as patient education a…
Fine-Tuned Large Language Models for Automated Radiology Impression Generation: A Multicenter Evaluation.
Purpose To develop a fine-tuned large language model (Medical Imaging Report Assistant, MIRA) and evaluate its performance in generating radiology impressions from multicenter dat…
A Multi-AI Agent Framework for Interactive Neurosurgical Education and Evaluation: From Vignettes to Virtual Conversations.
BACKGROUND AND OBJECTIVES: Traditional medical board examinations present clinical information in static vignettes with multiple-choices (MC), fundamentally different from how phy…
2026-04-15
Probabilistic Feature Imputation and Uncertainty-Aware Multimodal Federated Aggregation
Multimodal federated learning enables privacy-preserving collaborative model training across healthcare institutions. However, a fundamental challenge arises from modality heterog…
AbdomenGen: Sequential Volume-Conditioned Diffusion Framework for Abdominal Anatomy Generation
Computational phantoms are widely used in medical imaging research, yet current systems to generate controlled, clinically meaningful anatomical variations remain limited. We pres…
Multimodal large language models in brain tumor imaging: clinical applications and future perspectives.
The use of multimodal data is essential for the precise diagnosis and treatment of brain tumors. In this context, multimodal data encompass multisequence magnetic resonance imagin…
Bridging the Modality Gap in Medical Vision-Language Models: A Hybrid Contrastive-Optimal Transport Framework for Enhanced Cross-Modal Alignment.
Vision-language models in healthcare face a critical limitation, i.e., the modality gap, where image and text embeddings occupy distantly separated regions in shared representatio…
User Experience and Early Clinical Outcomes of a Mental Wellness Chatbot for Depression and Anxiety: Pilot Evaluation Mixed Methods Study.
BACKGROUND: Artificial intelligence-powered conversational agents (ie, chatbots) are increasingly popular outlets for users seeking psychological support, yet little is known abou…
Comparison of AI-based Chatbot Performance in Analyzing Clinical Scenarios versus Medical Residents: A Novel Approach in Chest Diseases Education.
OBJECTIVE: Rapid advancements in artificial intelligence (AI) technologies offer new opportunities in medical education. The aim of this study is to compare the performance of lar…
2026-04-14
GazeVaLM: A Multi-Observer Eye-Tracking Benchmark for Evaluating Clinical Realism in AI-Generated X-Rays
We introduce GazeVaLM, a public eye-tracking dataset for studying clinical perception during chest radiograph authenticity assessment. The dataset comprises 960 gaze recordings fr…
Budget-Aware Uncertainty for Radiotherapy Segmentation QA Using nnU-Net
Accurate delineation of the Clinical Target Volume (CTV) is essential for radiotherapy planning, yet remains time-consuming and difficult to assess, especially for complex treatme…
Efficient KernelSHAP Explanations for Patch-based 3D Medical Image Segmentation
Perturbation-based explainability methods such as KernelSHAP provide model-agnostic attributions but are typically impractical for patch-based 3D medical image segmentation due to…
Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models
Occlusion, where target structures are partially hidden by surgical instruments or overlapping tissues, remains a critical yet underexplored challenge for foundation segmentation…
Comparing Large Language Models and Traditional Machine Translation Tools for Translating Medical Consultation Summaries: Quantitative Pilot Feasibility Study.
BACKGROUND: Translation of medical consultation summaries is essential for equitable health care communication in culturally and linguistically diverse populations. While machine…
Toward Sustainable Clinical Analysis: Benchmarking Plastic Use in LC-MS Sample Preparation - Exemplified by Ketamine Analogues in Whole Blood.
The aim of this study was to assess and benchmark plastic consumption in sample preparation for forensic analysis, alongside the development of an LC-MS method for ketamine analog…
Diversity in clinical Trials: The example of systemic lupus erythematosus.
OBJECTIVE: The FDA requires clinical trials to reflect real-world diversity. Systemic lupus erythematosus (SLE) is a disease that disproportionately affects individuals of Black A…
Comparative Performance of Gemini 3 Pro and GPT-5 Family Models on Ophthalmology Board-Style Questions.
OBJECTIVE: To compare the performance of state-of-the-art Gemini and GPT models on ophthalmology board-style questions and examine variation by subspecialty, cognitive complexity,…
2026-04-11
Evaluating the clinical decision-making performance of large language models in clinically oriented thoracic anatomy scenarios: a comparative evaluation study.
Exploratory study of large language models in surgical decision-making for lumbar disc herniation: a multicenter analysis based on multisource clinical information.
A hybrid large language model framework for structured data entry from code-switched persian clinical speech.
Factors influencing large language model adoption among dental students: a cross-sectional study.
This research evaluates the factors influencing the behavioural intention (BI) to adopt large language models (LLMs) among dental students in education, clinical decision support…
2026-04-09
ClinicRealm: Re-evaluating large language models with conventional machine learning for non-generative clinical prediction tasks.
Large Language Models (LLMs) are increasingly deployed in medicine. However, their utility for non-generative clinical prediction is under-evaluated, and they are often assumed to…