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Performance Comparison of a Neuro-Symbolic Large Language Model System Versus Human Experts in Acute Cholecystitis Management.

Background/Objectives: Large language models (LLMs) have shown promising results in medical decision support; however, their effectiveness in managing acute cholecystitis and other gallbladder diseases remains insufficiently examined. This study evaluated the performance of a neuro-symbolic LLM system that integrates multiple AI agents with neural-symbolic reasoning for acute cholecystitis management and compared its diagnostic accuracy with that of human expert physicians across three clinical specialties. Methods: This multi-center cross-sectional study included 30 case-based questions cover…

Deaf Children's Use of Mutual Exclusivity and Eye Gaze to Determine Word Meanings in American Sign Language.

When determining the meaning of new words, young children have a variety of cues available to them. Two types of such cues are referential attention (e.g., gaze and point cues), and lexical constraints, such as mutual exclusivity biases. This is likely also true for deaf children acquiring American Sign Language (ASL), however, the visual modality of the language and the variation in individual early language experience may affect the extent to which they prioritize these cues in ways that are different from hearing children acquiring spoken language. In the current study, we examined the rela…

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 evaluates the accuracy and speed of large language models (LLMs) in automatically vetting lumbar spine MRI referrals from general practice.MethodsThree LLMs (GPT-4, Claude Opus, Gemini) were tasked with assigning an outcome (Accept - Routine, Accept - Urgent, Reject) and flagging MRI contraindications for lumbar spine referrals. Three prompts of increasing detail, including clinical guidelines and training examples, w…

The Role of Genetic Testing in Pediatric Expressive Language Delay: Evidence from the National Brain Gene Registry.

BACKGROUND/OBJECTIVES: Speech and language delay (SLD) is one of the most prevalent developmental conditions in childhood, with post-pandemic data indicating a notable increase in identified cases. Within this group, expressive language disorder (ELD) frequently appears alongside neurodevelopmental disorders such as autism spectrum disorder (ASD), epilepsy, and intellectual disability. Although awareness of ELD has grown, the role of genetic testing in its evaluation remains unclear, as such testing is not routinely pursued for isolated expressive language concerns. This gap highlights the nee…

Natural Language Processing Identification of Nonprescribed Fentanyl Use in Electronic Health Records: Algorithm Development and Validation Study.

BACKGROUND: Overdose and suicide due to nonprescribed fentanyl use have increased significantly, yet health care systems lack reliable methods to identify patients who use nonprescribed fentanyl. International Classification of Diseases codes are inconsistent and do not specify nonprescribed fentanyl use. OBJECTIVE: This study aimed to develop natural language processing approaches to identifying nonprescribed fentanyl use in electronic health record (EHR) documentation. METHODS: This retrospective study included Veterans Health Administration patients seen between April 5, 2023, and Decembe…

The Performance of Large Language Models in Extracting Intestinal Symptoms From Electronic Health Records: Retrospective Observational Study.

BACKGROUND: Unstructured electronic health records (EHRs) hinder the monitoring of intestinal infections. Large language models (LLMs) enable automated symptom extraction. However, their clinical validation is limited by a lack of systematic multimodel comparisons, unclear prompting strategies, and the privacy risks of cloud-based models (eg, data leakage and cross-border data transfer). OBJECTIVE: This study aimed to systematically evaluate the performance of locally deployed open-source LLMs across 4 model families in extracting intestinal symptoms from unstructured EHR chief complaints und…

Assessing the State of Published HIV Research Concerning Sexual Minority Women: A Global English Language Scoping Review.

PURPOSE: We conducted a scoping review to map peer-reviewed literature on HIV research concerning sexual minority women (SMW). METHODS: Following guidance from Arksey and O'Malley's framework and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews, we searched PubMed, Embase, PsycInfo, Sociological Abstracts, and CINAHL from inception through May 2023. Eligible studies were English language, peer-reviewed studies, containing disaggregated HIV-related data on SMW. Studies were systematically screened, and relevant data were extracted and then su…

Communication Barriers and Health Literacy Gaps Undermine Equitable Healthcare for Deaf Sign Language Users: A Qualitative Study.

Introduction/ObjectiveDeaf sign language users face persistent barriers to equitable healthcare, yet evidence from non-Western and lower-income contexts remains limited. This study explored the lived experiences of Deaf undergraduate students in Thailand, examining how communication barriers, health literacy gaps, and system design intersect to shape their access to equitable care.MethodsWe conducted a qualitative study using semi-structured, in-depth interviews with ten Deaf undergraduate students (aged 18-22 years). Participants were recruited through purposive sampling and interviews were c…

Воздействие средств АИ, основанных на крупных языковых моделях, на физиологические и пациентские связи: систематический обзор и мета-анализ.

Недавний прогресс в крупных языковых моделях (МЖЛ), таких, как GPT-3/4 :: стимулировали развитие болтовн искусственного интеллекта (AI) и консультативных инструментов в области медицины. Эти системы предназначены для оказания помощи или расширения коммуникации между врачом и пациентом, что может способствовать улучшению сочувствия, ясности и реагирования. Однако их фактическое воздействие на результаты коммуникационной деятельности остается неопределенным.2020-2025) оценка того, каким образом принимаемые на базе ЛЛД меры влияют на общение между врачом и пациентом, включая сочувствие, ясность,

Совпадение в аннотации оригинала
BackgroundRecent advances in large language models (LLMs) such as GPT-3/4 have spurred the development of artificial intelligence (AI) chatbots and advisory tools in medicine. These systems are posited to assist or augment physician-patient communication, potentially improving empathy, clarity, and responsiveness. However, their actual impact on communicatio…

Automated Detection of Cancer-Suspicious Findings in Japanese Radiology Reports with Natural Language Processing: A Multicenter Study.

Missed critical imaging findings, particularly those indicating cancer, are a common issue that can result in delays in patient follow-up and treatment. To address this, we developed a rule-based natural language processing (NLP) algorithm to detect cancer-suspicious findings from Japanese radiology reports. The dataset used consisted of chest and abdomen CT reports from six institutions. Reports from our institution were used for algorithm development and internal evaluation, while reports from the other five institutions were used for external evaluation. To create the gold standard, reports…

Enhancing brain tumor surgery precision with multimodal connectome imaging: Structural and functional connectivity in language-dominant areas.

OBJECTIVES: Language is a critical aspect of human cognition and function, and its preservation is a priority for neurosurgical interventions in the left frontal operculum. However, identification of language areas can be inconsistent, even with electrical mapping. The use of multimodal structural and functional neuroimaging in conjunction with intraoperative neuromonitoring may augment cortical language area identification to guide the resection of left frontal opercular lesions. METHODS: Structural and functional connectome scans were generated using a machine learning software to reparcell…

Evaluating AI-Generated Meal Plans for Simulated Diabetes Profiles: A Guideline-Based Comparison of Three Language Models.

AIMS: This synthetic simulation, using no real patient data, study aimed to evaluate and compare the performance of three prominent large language models (LLMs)-ChatGPT-4.1, Grok-3 and DeepSeek-in generating medical nutrition therapy aligned dietary plans for adults with type 2 diabetes mellitus (T2DM). METHODS: A simulation-based design was employed using 24 standardized virtual patient profiles differentiated by gender and body mass index (BMI) category. Each LLM was prompted in Turkish to generate 3-day meal plans. Outputs were assessed for energy and macro-/micronutrient accuracy, adheren…

Large Language Models for Diagnosing Focal Liver Lesions From CT/MRI Reports: A Comparative Study With Radiologists.

BACKGROUND & AIMS: Whether large language models (LLMs) could be integrated into the diagnostic workflow of focal liver lesions (FLLs) remains unclear. We aimed to investigate two generic LLMs (ChatGPT-4o and Gemini) regarding their diagnostic accuracies referring to the CT/MRI reports, compared to and combined with radiologists of different experience levels. METHODS: From April 2022 to April 2024, this single-center retrospective study included consecutive adult patients who underwent contrast-enhanced CT/MRI for single FLL and subsequent histopathologic examination. The LLMs were prompted …

Basque-Spanish Bilingual Aphasia: A Case-Study to Reveal Frequency-Based, Language-Agnostic Lexical Organization in Bilinguals.

This study investigated whether language serves as the primary organizational axis dividing lexico-semantic representations in multilingual individuals, or whether language is a subsidiary feature to dominant organizing principles identified in monolingual individuals. To address this question, we examined the influence of two well-established principles of language organization-frequency and concreteness-on naming accuracy in a post-stroke bilingual individual with anomic aphasia (PWA). The participant, a highly proficient Basque-Spanish bilingual, underwent MRI scanning to delineate the exte…

Amyloid-β Deposition Prediction With Large Language Model Driven and Task-Oriented Learning of Brain Functional Networks.

Amyloid- positron emission tomography can reflect the Amyloid- protein deposition in the brain and thus serves as one of the golden standard … tailored for this task. Our framework comprises three innovative components: 1) a pre-trained Large Language Model Nodal Embedding Encoder, designed to extract task-related features from fMRI signals; 2) a task-oriented Hierarchical-order FCN Learning module, used to enhance the representation of complex correlations among different brain regions for impro …

Spatiotemporal Dynamics of Language Information Representation within the Left Inferior Parietal Lobule Network: An Intracranial SEEG Study.

The left inferior parietal lobe (IPL) supports diverse cognitive functions through the finely differentiated yet coordinated interactions of its subregions, including the supramarginal gyrus (SMG), angular gyrus (AG), and lateral bank of the intraparietal sulcus (lbIPS). However, how these subregions dynamically represent and exchange information during cognitive processing remains unclear. Language processing, integrating visual form, phonology, and semantics provide an ideal model for investigating the IPL's functional capabilities and dynamics. To overcome the limitations of traditional neu…

Algorithmic Classification of Psychiatric Disorder-Related Spontaneous Communication Using Large Language Model Embeddings: Algorithm Development and Validation.

BACKGROUND: Language, which is a crucial element of human communication, is influenced by the complex interplay between thoughts, emotions, and experiences. Psychiatric disorders have an impact on cognitive and emotional processes, which in turn affect the content and way individuals with these disorders communicate using language. The recent rapid advancements in large language models (LLMs) suggest that leveraging them for quantitative analysis of language usage has the potential to become a useful method for providing objective measures in diagnosing and monitoring psychiatric conditions by…

A vision-language model for enhanced MCI identification in Alzheimer's disease through neuropsychological and neuroimaging data integration.

Alzheimer's Disease (AD) is a prevalent neurodegenerative disorder with the progression typically spanning from mild cognitive impairment (M … llenges due to the inherent heterogeneity across different data modalities. In this study, a Vision-Language Model (VLM)-based approach is proposed to enhance the identification of Alzheimer's Disease, with a particular focus on the early detection of MCI. Our contribution is a synergistic architecture that includes domain-specific learnable abnormality to …

Evaluating Retrieval-Augmented Large Language Models on External Cervical Resorption: A Comparative Study of Gemini and NotebookLM.

INTRODUCTION: This study evaluated the accuracy and consistency of two large language models developed by Alphabet Inc., Google Gemini (GG), a base configuration, and NotebookLM (NLM), a document-grounded configuration, when answering clinical questions regarding external cervical resorption using a retrieval-augmented framework. METHODS: Forty-six dichotomous clinical questions related to external cervical resorption were developed by three academic endodontists based on established sources. Each question was submitted to GG and NLM using three independent user accounts, yielding 276 respons…

Evaluating language model embeddings for Parkinson's disease cohort harmonization using a novel manually curated variable mapping schema.

Data Harmonization is an important yet time-consuming process. With the recent popularity of applications using Language Models (LMs) due to their high capabilities in text understanding, we investigated whether LMs could facilitate data harmonization for clinical use cases. To evaluate this, we created PASSIONATE, a novel Parkinson's disease (PD) variable mapping schema as a ground truth source for pairwise cohort harmonization using LLMs. Additionally, we extended our investigation using an existing Alzheimer's disease (AD) CDM. We computed text embeddings based on two language models to per…