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Mapping the Reliability-Readability Gap in the Education of Patients With Age-Related Macular Degeneration Across 6 Large Language Models: Comparative Evaluation Study.

BACKGROUND: Artificial intelligence-generated health information is increasingly used by patients, but its reliability, visible transparency indicators, and readability remain uncertain in specialized ophthalmic conditions such as age-related macular degeneration (AMD). OBJECTIVE: This study aimed to evaluate and compare the informational reliability, visible transparency indicators, overall quality, and readability of responses generated by 6 publicly accessible large language models (LLMs) to AMD-related patient-facing prompts under a zero-shot, single-turn prompting scenario. METHODS: Thi…

Exploring Attitudes of Primary Caregivers Towards Pediatric Tissue-Based Research using Large Language Models: Insights from Rural and Urban Community Calls and Surveys.

OBJECTIVES: Explore the perspectives of primary caregivers towards pediatric tissue-based research participation. DESIGN: Cross-sectional. SETTING: Two academic pediatric gastroenterology sites in the United States, including one serving a largely rural referral population and one urban clinic population. PARTICIPANTS: Primary caregivers of children who underwent endoscopy between 2017-2018 at UVA or were seen in the clinic setting between 2024-2025 at Tulane and referred by their child's gastroenterologist to complete an electronic survey. MEASURES AND ANALYSIS: Primary caregiver attitude

Diagnostic capability of large language models in critically ill patients: a prospective single-centre study comparing ChatGPT, Claude, and Gemini with emergency physicians.

BACKGROUND: Clinical decision-making requires integrating history, physical examination, laboratory, and imaging data. In the emergency department (ED), workload, time pressure, and cognitive burden may impair this process and affect decision quality. This study compares the diagnostic outputs of ChatGPT, Claude, and Gemini with those of emergency physicians in real-world ED cases. METHODS: This prospective, single-centre observational diagnostic agreement study compared the stage-wise outputs of four Large Language Models (LLMs) (ChatGPT-4o, ChatGPT-5, Claude Opus 4.1, and Gemini 2.5 Pro) wi…

A Clinical Quality-Assurance Panel for Patient-Facing Black-Box Large Language Models in Healthcare: Anchored Forced-Choice Drift Monitoring for Sensitive Communication.

Background Patient-facing large language models (LLMs) are being integrated into patient portals, automated health messaging, psychoeducation, and mental health-related support. Because many deployments are vendor-hosted black boxes that may change without local visibility, health systems need practical quality-assurance methods that can establish a baseline behavioral signature and flag candidate changes in how sensitive topics are framed. Methods This feasibility study applied an anchored two-alternative forced-choice (2AFC) protocol to ChatGPT 5.4 (OpenAI, San Francisco, California, United …

Assessment of patient information quality provided by artificial intelligence-based large language models on cryptorchidism: a comparison of ChatGPT-5, Gemini, and Grok.

OBJECTIVE: The aim of this study was to evaluate the responses generated by ChatGPT-5, Gemini, and Grok to the "six most frequently asked pa … luations were performed by four urologists. RESULTS: Among the three artificial intelligence-based large language models, Gemini achieved the highest mean Quality Assessment Tool for Patient Health Information and Patient Education Materials Assessment Tool for Printable Materials scores. Kruskal-Wallis analysis demonstrated a statistically significant difference in Q …

Toward Automating the Selection of Articles Reporting EQ-5D Data for Systematic Literature Reviews Using Large Language Models: Algorithm Development and Evaluation Study.

BACKGROUND: Systematic literature reviews (SLRs) are essential for evidence synthesis in health research but remain labor-intensive, especially at the screening stage. Manual review of titles and abstracts requires substantial human effort, while existing automation tools still have limited adoption in health technology assessment. The EQ-5D questionnaire, a widely used patient-reported outcome measure for health-related quality of life, provides data that frequently underpin reimbursement and policy decisions. OBJECTIVE: This pilot study evaluated whether recent large language models (LLMs) …

Contextual insights from large language models in ambulatory assessment: Differential reactivity of affect and self-esteem to daily events in borderline personality disorder.

Ambulatory assessment (AA) research often relies on closed-ended context checklists, whereas open-ended event descriptions preserve richer idiographic context but are rarely analyzed at scale. Borderline personality disorder (BPD) is characterized by marked instability, providing a clinically informative use case for testing scalable approaches to contextual dynamics in daily life. We investigated the association of event context and sentiment (i.e., emotional connotation) derived using a large language model (LLM) with self-reported momentary calmness, affective valence, and self-esteem in a

Limitations in Chest X-Ray Interpretation by Vision-Capable Large Language Models, Gemini 1.0, Gemini 1.5 Pro, GPT-4 Turbo, and GPT-4o.

Background/Objectives: Interpretation of chest X-rays (CXRs) requires accurate identification of lesion presence, diagnosis, location, size, and number to be considered complete. However, the effectiveness of large language models with vision capabilities (LLMs) in performing these tasks remains uncertain. This study aimed to evaluate the image-only interpretation performance of LLMs in the absence of clinical information. Methods: A total of 247 CXRs covering 13 diagnostic categories, including pulmonary edema, cardiomegaly, lobar pneumonia, and other conditions, were evaluated using Gemini 1…

The accuracy of large language models in assessing tooth detection and identification, dental caries, periapical status, and tooth development from mixed dentition radiographs.

BACKGROUND: Various studies have evaluated the accuracy of large language models (LLMs) in the detection of periapical radiolucencies from adult radiographs and orthopantomographs. However, none have evaluated their efficacy in evaluating parameters from mixed dentition radiographs. AIM: The aim of this study was to evaluate the accuracy of GPT-5 (GT), Gemini 3 Pro (GP), and Sonar (SR) in assessing tooth detection and identification, dental caries, periapical status, and stages of tooth development. SETTINGS AND DESIGN: A total of 93 mixed dentition radiographs from children aged 6-12 years …

Leveraging Large Language Models for Infectious Disease Surveillance-Using a Web Service for Monitoring COVID-19 Patterns From Self-Reporting Tweets: Content Analysis.

BACKGROUND: The emergence of new SARS-CoV-2 variants, the resulting reinfections, and post-COVID-19 condition continue to impact many people … -speaking annotators annotated a training dataset of COVID-19-positive self-reporters. We then used large language models to identify positive self-reporters from other unannotated tweets. We used the Hibert transform to calculate the lead of the prediction curve ahead of the reported curve. Finally, we presented our findings on symptoms, recovery, reinfections, and lo …

Large Language Models for the National Radiological Technologist Licensure Examination in Japan: Cross-Sectional Comparative Benchmarking and Evaluation of Model-Generated Items Study.

BACKGROUND: Mock examinations are widely used in health professional education to assess learning and prepare candidates for national licensure. However, instructor-written multiple-choice items can vary in difficulty, coverage, and clarity. Recently, large language models (LLMs) have achieved high accuracy in medical examinations, highlighting their potential for assisting item-bank development; however, their educational quality remains insufficiently characterized. OBJECTIVE: This study aimed to (1) identify the most accurate LLM for the Japanese National Examination for Radiological Techn…

Extraction of Pain Severity and Functional Interference From Clinical Narratives Using Domain-Informed Large Language Models: Protocol for a Development and Validation Study.

BACKGROUND: Chronic pain is a leading cause of disability and requires multidimensional assessment of pain intensity and functioning, yet electronic health records rarely capture these measures systematically. By contrast, surveys collecting patient-reported outcomes can assess pain over multiple dimensions but remain resource-intensive and difficult to scale for continuous population-level monitoring. OBJECTIVE: The objective of this study is to develop and validate a domain-informed natural language processing framework to derive pain severity and functional interference outcomes from unstr

Comparative Performance of Large Language Models in Urodynamic Trace Interpretation Using an Adapted Expert-Led Evaluation of Generative AI Competence and Excellence Framework.

OBJECTIVES: Interpretation of urodynamic studies is complex and subject to substantial inter-observer variability, even among experienced clinicians. While artificial intelligence has shown promise in urology, evidence regarding the performance of large language models (LLMs) in urodynamic trace interpretation remains limited. This study aimed to systematically compare the performance of multiple contemporary LLMs in interpreting standard urodynamic traces using an adapted ELEGANCE framework. METHODS: In this retrospective, single-centre comparative study, 119 anonymised urodynamic machine pr…

The Evolving Security of the Internet of Vehicles: A Survey from Classical Machine Learning to Large Language Models, Adversarial Robustness, and Explainable AI.

The Internet of Vehicles (IoV) connects vehicles, roadside infrastructure, and cloud or edge platforms to support safer and more efficient transportation. This connectivity also exposes vehicular networks to message spoofing, denial-of-service, and man-in-the-middle attacks, and to threats that target the machine-learning defenses themselves, such as model poisoning, gradient inversion, and adversarial examples. Machine learning and deep learning are now the dominant defensive tools, and the resulting literature is large and fragmented. This survey reviews that literature using a documented da

Online patient information on elective orthognathic surgery provided by UK National Health Services hospitals: assessment of readability and improvement using large language models.

Clear and understandable patient information is crucial for both informed consent and postoperative recovery following elective orthognathic surgery. Despite National Health Service (NHS) guidance recommending suitability for the reading level of an 11-year-old, current patient information leaflets (PILs) do not meet this standard. This study was undertaken to assess the readability of NHS orthognathic surgery patient information and whether large language models (LLMs) could improve readability to meet the recommended levels. A cross-sectional analysis was conducted of online PILs on elective…

Extracting smoking history from clinical notes for lung cancer screening decision support: comparing a structured-judgment model with general-purpose large language models.

OBJECTIVE: To compare the accuracy, cost, and speed of a low-cost, non-generative structured-judgment model and four general-purpose large language models (LLMs) for extracting smoking status, pack-years, and quit date from clinical notes to support lung cancer screening (LCS) clinical decision support (CDS). MATERIALS AND METHODS: We built a synthetic, shareable benchmark of 3,000 outpatient notes in three conditions: 1,000 template-generated notes, 1,000 realistic, ``messy'' notes written by an LLM from structured facts, and 1,000 ``messy'' notes that required complex arithmetic to determin…

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

Chimeric Antigen Receptor T-cell (CAR‑T) therapy, genetically engineered patient T cells targeting tumor antigens, has transformed care for hematologic malignancies but requires careful tracking of adverse events (AEs) often documented only in unstructured electronic health record (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

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… Leveraging large language models (LLMs) to extract these patterns offers a promising way to better characterize AE trajectories and ultimately improve monitoring and outcomes for CAR-T recipients. … Large Language Models for Adverse Drug Events: A Clinical Perspective. J Clin Med. 2025;14(15):5490. doi: 10.3390/jcm14155490 41. Ludwig D, Wang M, Buchanan J, Trinh T. A Large Language Model for Extracting Post-marketing Adverse Drug Events from Clinical Notes in the Electronic Health Record. …

Real-world text-only inference of PI-RADS v2.1 from prostate MRI reports using large language models: a lesion-level, zone-aware study.

PURPOSE: To evaluate the feasibility and limitations of real-world, text-only inference of PI-RADS v2.1 categories from prostate MRI reports using large language models, with lesion-level and zone-aware analysis. MATERIALS AND METHODS: This single-center retrospective study included 1,205 lesion-level entries from 1,118 patients derived from semi-structured prostate MRI reports after removal of all explicit PI-RADS elements. ChatGPT-4o was prompted to assign numeric PI-RADS categories based solely on report text. Agreement with radiologist-assigned reference categories was assessed using exac…