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Perception of Synthesized Mandarin Speech Based on a Large-Scale Language Model Among Deaf Adults With Cochlear Implants.

PURPOSE: This study investigated the intelligibility and processing characteristics of Mandarin sentences synthesized based on a large-scale language model (LLM), compared to natural speech, specifically for deaf adults with cochlear implants (CIs). METHOD: Fifty participants, 25 Mandarin-speaking deaf adults with CIs and 25 Mandarin-speaking adults with normal hearing (NH), were enrolled in the study. Sentence recognition rates and reaction times were measured using sentences from the Mandarin speech perception database presented as six different speech types. These included original natural

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…

Personalized CT Protocol Recommendation via Large Language Model: Enabling Fully Automated CT Scanning Workflows.

Manual CT protocol selection persists as a time-intensive and error-prone bottleneck in radiology workflows, impeding the realization of fully automated scanning pipelines. To overcome this limitation, we developed a Large Language Model Retrieval-Augmented Generation (LLM-RAG) framework for personalized CT protocol recommendation. This system constructs a protocol knowledge base from historical examination records to deliver institutionally tailored, precision recommendations aligned with clinical preferences. Our system demonstrated compelling performance (min: 88.60% precision, 89.34% recal

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 standards for Alzheimer's disease (AD) diagnosis. However, its practical cost and high radioactivity hinder its application in large-scale early AD screening. Recent neuroscience studies suggest a strong association between changes in functional connectivity network (FCN) derived from functional MRI (fMRI), and deposition patterns of Amyloid- protein in the brain. This enables an FCN-based approach to assess the Amyloid- protein deposition with less expense and ra

Development and preliminary testing of a secure large language model-based chatbot for brief alcohol counseling in young adults.

OBJECTIVE: Young adults face elevated risks from alcohol use yet encounter significant barriers to accessing evidence-based interventions. Large language models (LLMs) represent a promising advancement for delivering personalized behavioral interventions, but their application to alcohol counseling remains unexplored. This study evaluated the development and preliminary outcomes of a Secure GPT-4-powered text-based Motivational Interviewing Conversational Agent (MICA). METHOD: Using a prospective single-arm pilot design, we evaluated MICA across two phases (Phase I: n = 8; Phase II: n = 37), …

Identifying Key Predictors of Smoking Cessation Success: Text-Based Feature Selection Using a Large Language Model.

BACKGROUND: The most effective way to reduce mortality and morbidity among current smokers is to quit smoking. Although about half of smokers attempted to quit, only one-tenth succeeded in 2022. OBJECTIVE: To identify key predictors of smoking cessation success to inform cessation interventions and increase quitting rates. METHODS: We analyzed data from waves 5 and 6 of the Population Assessment of Tobacco and Health (PATH) study (December 2018 to November 2021). Using OpenAI's GPT-4.1, we identified the top 45 variables from wave 5 that are highly predictive of 12-month smoking abstinence i

Is Large Language Model-Assisted Citation Screening Feasible in a Scoping Review on Nonpharmacological Interventions for Delirium in Patients With Cancer?

Systematic and scoping reviews are essential in palliative care, yet they are time-consuming and resource-intensive. Recent advancements in artificial intelligence, particularly large language models (LLMs), have shown promise in enhancing the efficiency of literature screening. However, their feasibility and accuracy in scoping reviews remain unclear. In this study, we aimed to evaluate the feasibility and performance of LLM-assisted citation screening for a scoping review on nonpharmacological interventions for delirium in patients with cancer. This prospective simulation study assessed the …

Enhancing Diagnostic Precision: Utilising a Large Language Model to Extract U Scores from Thyroid Sonography Reports.

This study evaluates the performance of ChatGPT-4, a Large Language Model (LLM), in automatically extracting U scores from free-text thyroid ultrasound reports collected from University Hospitals Birmingham (UHB), UK, between 2014 and 2024. The LLM was provided with guidelines on the U classification system and extracted U scores independently from 14,248 de-identified reports, without access to human-assigned scores. The LLM-extracted scores were compared to initial clinician-assigned and refined U scores provided by expert reviewers. The LLM achieved 97.7% agreement with refined human U scor

A Large Language Model Workflow for Auditable Brain Abscess Risk Stratification and Pre-residency Scholarship: A Technical Report.

Large language models (LLMs) are increasingly available to medical trainees, but transparent and auditable methods for incorporating them into scholarly work and emerging artificial intelligence (AI) literacy curricula remain limited. This technical report describes a mentored educational framework in which a de-identified brain abscess case report was transformed into a reproducible, LLM-supported risk-stratification model to teach rigor, verification, and structured AI use to a fourth-year medical student. A single clinical case was reconstructed using structured variables from history, imag…

Large Language Model-Supported Identification of Intellectual Disabilities in Clinical Free-Text Summaries: Mixed Methods Study.

BACKGROUND: Free-text clinical data are unstructured and narrative in nature, providing a rich source of patient information, but extracting … ck gold-standard annotated references and other purpose-built resources, including software. Recent large language models (LLMs) can understand natural language instructions, which help them adapt to different domains and tasks without the need for specific training data. This makes them suitable for clinical applications, though their use in this field is limited. OB …

The cathartic dream: Using a large language model to study a new type of functional dream in healthy and clinical populations.

According to some theories of emotion regulation, dreams could modify negative emotions and ultimately reduce their intensity. We introduce here the idea of cathartic dream, a specific and separate type of emotional dream, which is characterized by a dynamic plot with emotional twists, and where negative emotions are expressed and ultimately decreased. This process would reflect psychological relief (catharsis according to the Aristotelian definition) and fulfil an emotion regulation function. We developed and validated a tool using a large language model to emotionally categorize the differen

The performance of artificial intelligence large language model-linked chatbots in surgical decision-making for gastroesophageal reflux disease.

BACKGROUND: Large language model (LLM)-linked chatbots may be an efficient source of clinical recommendations for healthcare providers and patients. This study evaluated the performance of LLM-linked chatbots in providing recommendations for the surgical management of gastroesophageal reflux disease (GERD). METHODS: Nine patient cases were created based on key questions addressed by the Society of American Gastrointestinal and Endoscopic Surgeons (SAGES) guidelines for the surgical treatment of GERD. ChatGPT-3.5, ChatGPT-4, Copilot, Google Bard, and Perplexity AI were queried on November 16th

Recommendations for diabetic macular edema management by retina specialists and large language model-based artificial intelligence platforms.

PURPOSE: To study the role of artificial intelligence (AI) in developing diabetic macular edema (DME) management recommendations by creating and comparing responses to clinicians in hypothetical AI-generated case scenarios. The study also examined whether its joint recommendations followed national DME management guidelines. METHODS: The AI hypothetically generated 50 ocular case scenarios from 25 patients using keywords like age, gender, type, duration and control of diabetes, visual acuity, lens status, retinopathy stage, coexisting ocular and systemic co-morbidities, and DME-related retina

Extraction of clinical data on major pulmonary diseases from unstructured radiologic reports using a large language model.

Despite significant strides in big data technology, extracting information from unstructured clinical data remains a formidable challenge. This study investigated the utility of large language models (LLMs) for extracting clinical data from unstructured radiological reports without additional training. In this retrospective study, 1800 radiologic reports, 600 from each of the three university hospitals, were collected, with seven pulmonary outcomes defined. Three pulmonology-trained specialists discerned the presence or absence of diseases. Data extraction from the reports was executed using G…

Anesthetic Management of a Patient With Juvenile Hyaline Fibromatosis: A Case Report Written With the Assistance of the Large Language Model ChatGPT.

This case report was written with the assistance of the large language model known as ChatGPT, a form of generative artificial intelligence that can write grammatically correct and semantically meaningful prose on a multitude of topics. Here, it has assisted us in presenting a case of anesthetic management for a case of Juvenile Hyaline Fibromatosis (JHF), an extremely rare genetic disorder that is part of a spectrum of diseases presently characterized as Hyaline Fibromatosis Syndrome (HFS), which also includes a more severe variant presenting in infancy. HFS is caused by autosomal recessive m

When the Algorithm Becomes the Dietitian: Large Language Model Hallucinations, Algorithmic Food Environments, and the Erosion of Nutritional Autonomy.

Large language models (LLMs) can answer dietary questions, but their use as substitutes for registered dietitians remains insufficiently quantified. Evaluations have identified inaccurate nutrient targets, incomplete clinical advice, and insufficient personalization, alongside useful performance on some general nutrition tasks. Social media and food-delivery interfaces may also influence dietary choices through food cues, social endorsement, and choice architecture. This conceptual perspective presents ADRIFT (Algorithmic Dietary Reconfiguration through Informational, Framing, and Technologica…

Large language model-guided high-throughput screening for accelerated discovery of Pt-based high-entropy electrocatalysts.

Pt-based high-entropy alloy (Pt-HEA) catalysts show significant potential for the hydrogen evolution reaction (HER), yet their vast compositional space challenges conventional trial-and-error approaches. Here, we propose a large language model (LLM)-guided closed-loop framework for high-throughput discovery to accelerate the rational design and optimization of Pt-HEA catalysts. The framework first extracts structured knowledge from extensive literature and incorporates retrieval-augmented generation (RAG) to develop an LLM assistant tailored to HEA electrocatalysis research. The assistant tran

Multiclass diagnostic performance and error patterns of a multimodal large language model in oral histopathology: A WHO-aligned study.

OBJECTIVES: To evaluate the diagnostic performance, feature-level agreement, and error patterns of a multimodal large language model (MLLM) across the oral dysplasia-malignancy continuum. METHODS: In this retrospective diagnostic accuracy study, 300 histopathology images representing normal oral mucosa (n = 100), oral potentially malignant disorders (OPMD) (n = 100), and oral squamous cell carcinoma (OSCC) (n = 100) were evaluated using a standardized two-stage prompting framework consisting of morphological feature extraction followed by structured diagnostic classification. Reference diagno

MPAExpo-LM: Fine-Tuned Large Language Model for Mycophenolic Acid Exposure Estimation After Renal Transplantation.

An accurate estimation of mycophenolic acid (MPA) exposure after renal transplantation is important for reducing acute rejection in patients treated with mycophenolate mofetil (MMF) or enteric-coated mycophenolate sodium (MPS). We propose MPAExpo-LM, a fine-tuned large language model with a differential attention denoising module for this task. Experiments on real-world datasets demonstrate that MPAExpo-LM achieves superior results compared to existing methods in the task, offering a new pathway for precise immunosuppressive therapy. It attains an RMSE of 8.78 mg·h/L using three sampling point