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A large language model for delirium prediction in the intensive care unit using structured electronic health records.

Delirium is an acute syndrome characterized by fluctuating attention, cognitive impairment, and severe disorganization of behavior, which has been shown to affect up to 31% of patients in the intensive care unit (ICU). Early detection can enable timely interventions and improved health outcomes. While artificial intelligence (AI) models have shown great potential for ICU delirium prediction using structured electronic health records (EHR), most studies have either not leveraged state-of-the-art AI models, been limited to single-center cohorts, or relied on small datasets for development and va

A large language model-based tool for identifying relationships to industry in research on the carcinogenicity of benzene, cobalt, and aspartame.

BACKGROUND: Industry-funded research poses a threat to the validity of scientific inference on carcinogenic hazards. Scientists require tools to better identify and characterize industry sponsored research across bodies of evidence to reduce the possible influence of industry bias in evidence synthesis reviews. We applied a novel large language model (LLM)-based tool named InfluenceMapper to demonstrate and evaluate its performance in identifying relationships to industry in research on the carcinogenicity of benzene, cobalt, and aspartame. METHODS: All epidemiological, animal cancer, and mec

Comparative evaluation of large language model-based chatbots in a septic arthritis scenario: ChatGPT, Claude, and Perplexity.

OBJECTIVE: This study aimed to comparatively evaluate the clinical knowledge generation performance of 3 widely used large language model (LLM)-based chatbots (ChatGPT, Claude, and Perplexity) in the context of septic arthritis. METHODS: This cross-sectional comparative study was based on 24 scenario-based clinical questions developed in accordance with the SANJO guideline (Management of Septic Arthritis in Native Joints) of the European Bone and Joint Infection Society. Responses generated by ChatGPT (OpenAI GPT-4), Claude 2 (Anthropic), and Perplexity AI were independently assessed by 2 sen

A Prospective Controlled Trial of Large Language Model-based Simplification of Oncologic CT Reports for Patients with Cancer.

Background Radiology staging reports (ie, oncologic reports) are written for referring physicians using complex medical terminology. Large language models (LLMs) show promise for simplifying medical text for patient use, but controlled studies evaluating the impact of LLM simplification on patients' comprehension of radiology reports are lacking. Purpose To evaluate whether LLM-based simplification of oncologic CT reports improves patients' cognitive workload, text comprehension, perception, and reading time. Materials and Methods This prospective, controlled, open-label, quasi-randomized tria…

Customized GPT-4V(ision) for radiographic diagnosis: can large language model detect supernumerary teeth?

BACKGROUND: With the growing capabilities of language models like ChatGPT to process text and images, this study evaluated their accuracy in detecting supernumerary teeth on periapical radiographs. A customized GPT-4V model (CGPT-4V) was also developed to assess whether domain-specific training could improve diagnostic performance compared to standard GPT-4V and GPT-4o models. METHODS: One hundred eighty periapical radiographs (90 with and 90 without supernumerary teeth) were evaluated using GPT-4 V, GPT-4o, and a fine-tuned CGPT-4V model. Each image was assessed separately with the standardi

Обнаружение новых листьев с использованием большой языковой модели: прикладные программы в массивах данных с множественным склерозом в реальном мире.

ЦЕЛЬ: Нейроимация обычно используется для выявления новых видов подстрекательской деятельности при множественном склерозе (МС). Крупная языковая модель классификации описей магнитного резонанса в электронной медицинской записи (ЭПЧ) в качестве дискретных данных может принести значительные выгоды для исследований в области НР. Цели нынешнего исследования заключались в разработке такой оперативной иллюстрации его исследовательской деятельности на основе общего клинического сценария: мониторинг реакции на разрушающую клетки B (BCDT). МЕТОДЫ: Институциональная экосистема, которая надежно увязывает

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… Among the many applications of artificial intelligence (AI)‐enabled large language models (LLMs) in medicine, 1 , 2 is their ability to derive discrete metrics from the massive amounts of narrative data in electronic health records (EHRs). … msLesionprompt: Development and multistep validation of a large language model (LLM) to extract discrete data about new inflammatory activity from magnetic resonance imaging (MRI) reports in multiple sclerosis (MS). A total of 14,888 MRI reports were analyzed. …

Multimodal Large Language Models for Prognostic Prediction in Cervical Cancer Treated with Definitive Chemoradiotherapy: An Exploratory Study of Systematic Multimodal Data Integration.

BACKGROUND/OBJECTIVES: About 25-35% of patients with cervical cancer treated with definitive concurrent chemoradiotherapy (CCRT) relapse within five years, and non-imaging clinical factors stratify their risk only modestly. We evaluated whether a general-purpose multimodal large language model (MLLM), used without fine-tuning, could estimate recurrence risk in this setting. METHODS: In this retrospective single-center study, 82 patients treated with definitive radiotherapy (79/82 with concurrent platinum-based chemotherapy) who had a complete pretreatment MRI report were analyzed. Gemini 3.1

Performance evaluation of mainstream large language models in cataract science popularization Q&A: a comparative study of readability, quality, and educational suitability.

BACKGROUND: Cataract is one of the main causes of visual impairment and reversible blindness worldwide, and it mainly affects the elderly population. Clinically accurate and sufficiently readable patient education materials play a crucial role in this regard. With the rapid development of large language models (LLMs), patients are increasingly obtaining health information generated by artificial intelligence (AI); however, the reliability of online medical information is often questionable. This study systematically evaluated the readability, quality, and educational suitability of mainstream …

Benchmarking large language models on a Chinese radiation oncology technology examination-preparation question set: accuracy, consensus, and efficiency for AI assisted education.

PURPOSE: This study evaluated GPT-4o, GPT-5.4, and two DeepSeek platform configurations using 1, 053 examination-preparation questions from a publicly and commercially available 2025 exercise collection for the Chinese National Radiation Oncology Technology Qualification Examination (Intermediate Level). We assessed accuracy rates, identical-response coverage, accuracy consensus, and model-reported latency estimates. METHODS: Four configurations-GPT-4o, GPT-5.4, DS-Fast, and DS-Expert-were tested using an identical zero-shot Chinese prompt. Accuracy rates were summarized with Wilson 95% confi

Evaluation of large language models for PI-RADS score extraction from free-text prostate MRI reports: a comparative study with human readers.

OBJECTIVE: This study aimed to evaluate the ability of GPT-4o and Gemini 2.5 Pro to extract and assign PI-RADS v2.1 score from free-text prostate MRI reports, and compare their performance with human readers of varied experience. METHODS: Three radiologists with differing levels of experience (resident, fellow, expert) independently reviewed the reports and assigned PI-RADS v2.1 scores. The same reports were processed through prompts with the GPT-4o and Gemini 2.5 Pro. Inter-rater agreement was evaluated using Gwet's AC1 coefficient, and the diagnostic performance was assessed using sensitivi

Evaluating large language models vs residents in cataract and refractive surgery: comparative analysis using the American Academy of Ophthalmology Self-Assessment Program.

PURPOSE: To assess the accuracy of leading large language models (LLMs) in answering cataract and refractive surgery questions, determine whether prompt complexity affects performance, and compare their accuracy with ophthalmology residents. SETTING: Not applicable. DESIGN: Randomized questionnaire-based study using 100 questions from the cataract and refractive surgery section of the Basic and Clinical Science Course (BCSC) Self-Assessment Program. METHODS: 5 LLMs (ChatGPT-4, ChatGPT-4o, Gemini, Gemini Advanced, and Copilot-Precise Mode) were tested. Data from 1983 BCSC Self-Assessment Pro…

Extracting adverse event nature, severity, timelines and resulting interventions from clinical notes of patients receiving CAR-T therapy using large language models.

Chimeric Antigen Receptor T-cell (CAR-T) therapy, where genetically engineered patient T cells target tumor antigens, has transformed care for hematologic malignancies but requires careful tracking of adverse events (AEs) often documented only in unstructured EHR notes. We evaluated a Large Language Model (LLM)-based approach in UCSF's secure environment to extract AEs, dates, grades, and interventions within 30 days post-infusion for six commercial CAR-T products (2012-2023), benchmarking against two evaluators. Using GPT 4 0314 in a zero shot setting with four prompts (prespecified AEs, non

A Clinician-Centered Evaluation Framework for Large Language Models in Patient Education: Integrating the Technology Acceptance Model and Medical Condition Regard Scale.

Inadequate postcare patient education contributes to preventable readmissions and adverse outcomes that disproportionately affect medically complex, high-need communities. Large language models (LLMs) show promise for generating personalized, plain-language patient education at scale. However, existing LLM evaluation frameworks prioritize technical accuracy over patient accessibility and alignment with health literacy, and few explicitly account for the attitudinal influences that clinician evaluators may introduce into the rating process. In this viewpoint, we introduce an evaluation framewor…

Benchmarking large language models on persian surgical subspecialty board examinations: a comparative study of ChatGPT-4o, ChatGPT-5, and Gemini 2.5 Flash.

This study evaluated the performance of three large language models, including ChatGPT-4o, ChatGPT-5, and Gemini 2.5 Flash, on 532 Persian multiple-choice questions from the 2025 Iranian surgical subspecialty board examinations. Questions spanned five domains: Pediatric, Cardiovascular, Vascular and Endovascular, Thoracic, and Plastic & Reconstructive Surgery. Using standardized prompts, we assessed overall accuracy, variation across subspecialties and question types, and the effect of question length. Gemini 2.5 Flash and ChatGPT-5 achieved higher accuracy (73.9% and 73.3%) than ChatGPT-4o (6…

Optimising Large Language Models-Based Automated Question Generation for Dental Pharmacology: A Quantitative Evaluation of a Multi-File JavaScript Object Notation Architecture.

INTRODUCTION: The application of Large Language Models (LLMs) for automated question generation in dental education is hindered by inefficient data management, where conventional systems using a single data file incur high Application Programming Interface (API) costs and slow performance due to excessive token consumption. This study aimed to quantitatively validate the performance benefits of an optimized, multi-file JavaScript Object Notation (JSON)-based architecture over this conventional approach. MATERIALS AND METHODS: We developed a system using Gemini 2.5 Pro and compared two archite…

Performance evaluation of five large language models for assisting in the interpretation of urinalysis reports for kidney diseases: a real-world study.

OBJECTIVES: Large language models (LLMs) show promise for interpreting laboratory reports, yet real-world validation remains limited. This study evaluated five advanced LLMs in interpreting urinalysis reports for kidney diseases using real-world clinical data, providing empirical evidence for the utility of LLM-assisted result interpretation. METHODS: We retrospectively collected 120 urinalysis reports from patients with primary glomerular diseases and secondary nephropathies. The testing platforms included the Sysmex UF5000 and Mindray EU8600. Five LLMs (ChatGPT-5, Claude-4.5, Gemini-2.5, De…

Profile-associated financial and access-related framing in LLM-generated pediatric asthma referral plans: a factorial audit of seven large language models.

BACKGROUND: Large language models (LLMs) are increasingly considered for clinical documentation, referral support, and patient-facing communication. Biomedical accuracy alone may be insufficient for safe deployment if generated plans vary in access navigation, financial-access language, referral specificity, or tone across socially meaningful patient cues. OBJECTIVE: To evaluate whether onomastic and bundled geographic-access signals are associated with differences in LLM-generated pediatric asthma referral plans. METHODS: We conducted a cross-sectional 2 × 2 factorial audit of seven commerc…

Automated PROMISE V2 Scoring from PSMA PET/CT Reports Using Large Language Models: A Comparative Evaluation of Prompt Design and Model Performance.

Large language models (LLMs) are increasingly explored for clinical use. However, the extent to which such models can reliably support physicians in reporting, staging, and the assessment of classification remains an active area of research. This study aimed to evaluate and compare multiple LLMs for automated PROMISE V2 classification for prostate cancer. A total of 126 unambiguous German-language PSMA PET/CT text reports were retrospectively analyzed, with reference standards established by expert consensus based on image interpretation and the original report text. Five LLMs (GPT-5.4, DeepSe…