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Can off-the-shelf visual large language models detect and diagnose ocular diseases from retinal photographs?

BACKGROUND: The advent of generative artificial intelligence has led to the emergence of multiple vision large language models (VLLMs). This study aimed to evaluate the capabilities of commonly available VLLMs, such as OpenAI's GPT-4V and Google's Gemini, in detecting and diagnosing ocular diseases from retinal images. METHODS AND ANALYSIS: From the Singapore Epidemiology of Eye Diseases (SEED) study, we selected 44 representative retinal photographs, including 10 healthy and 34 representing six eye diseases (age-related macular degeneration, diabetic retinopathy, glaucoma, visually significa…

Enhancing the Readability of Online Pediatric Cataract Education Materials: A Comparative Study of Large Language Models.

PURPOSE: The purpose of this study was to assess large language models (LLMs) for enhancing the readability of online patient education materials (PEMs) on pediatric cataracts through multilingual adaptation, content retrieval, and prompt engineering. METHODS: This study included 103 PEMs presented in different languages and retrieved from diverse resources. Three LLMs (ChatGPT-4o, Gemini 2.0, and DeepSeek-R1) were used for content improvement. Readability was assessed for both the original and converted PEMs with multiple formulas. Different prompt engineering strategies for LLMs were also t…

Can Large Language Models Guide Aortic Stenosis Management? A Comparative Analysis of ChatGPT and Gemini AI.

OBJECTIVE: Management of aortic stenosis (AS) requires integrating complex clinical, imaging, and risk stratification data. Large language models (LLMs) such as ChatGPT and Gemini AI have shown promise in healthcare, but their performance in valvular heart disease, particularly AS, has not been thoroughly assessed. This study systematically compared ChatGPT and Gemini AI in addressing guideline-based and clinical scenario questions related to AS. METHOD: Forty open-ended AS-related questions were developed, comprising 20 knowledge-based and 20 clinical scenario items based on the 2021 Europea…

The effectiveness of large language models in medical AI research for physicians: A randomized controlled trial.

Physicians offer invaluable clinical insights, but their involvement in medical AI research is hindered by limited technical expertise. We c … ashout, 41.2% of successful intervention participants complete a new project without the support of large language models (LLMs). A survey shows that 42.6% of participants fear regurgitating information without understanding and 40.4% worry about fostering lazy thinking, indicating potential dependency. Therefore, LLMs can help physicians overcome technical barriers, a …

Clinical Relevance of Large Language Models in Endodontics: Diagnostic Appropriateness Based on 50 Simulated Case Scenarios.

Large language models (LLMs) are increasingly used in healthcare, but their performance in endodontic decision-making remains unclear. This study aimed to compare six LLMs in terms of diagnostic appropriateness for endodontic treatment planning. Fifty clinical scenarios were developed and entered into six LLMs (ChatGPT-4o, ChatGPT-3.5, Claude 4, Copilot, DeepSeek-V3, Gemini 2.5). Two specialists scored responses as appropriate or inappropriate. Repeated measures ANOVA and chi-square tests were used for analysis. Claude showed the highest accuracy (76%), followed by DeepSeek and Gemini. ChatGPT…

Advancing the prediction and understanding of placebo responses in chronic back pain using large language models.

Placebo analgesia in chronic pain is a widely studied clinical phenomenon, where expectations about the effectiveness of a treatment can res … nt effects-which are well-known to play a role. Here, we assessed the potential of using fine-tuned large language models (LLMs) to predict placebo responders in chronic low-back pain using contextual features extracted from patient interviews, as they speak about their lifestyle, pain, and treatment history. We re-analyzed data from two clinical trials where individua …

Automating the Referral of Bone Metastases Patients With and Without the Use of Large Language Models.

BACKGROUND AND OBJECTIVES: Bone metastases, affecting more than 4.8% of patients with cancer annually, and particularly spinal metastases require urgent intervention to prevent neurological complications. However, the current process of manually reviewing radiological reports leads to potential delays in specialist referrals. We hypothesized that natural language processing (NLP) review of routine radiology reports could automate the referral process for timely multidisciplinary care of spinal metastases. METHODS: We assessed 3 NLP models-a rule-based regular expression (RegEx) model, GPT-4,

Evaluating the Efficacy of Large Language Models for Dizzy History Taking and Peripheral Vestibular Disorder Diagnosis.

ImportanceVertigo accounts for one of the most frequent presenting symptoms in primary care. However, complexities in differential diagnoses and reliance on clinical history contribute to frequent specialist referrals and diagnostic delays. Large language models (LLMs), like LLaMA-3.1-8B, offer new potential for assisting in clinical decision-making.ObjectiveTo assess the utility of a small-scale, open-source LLM in diagnosing peripheral vestibular disorders (PVDs), and evaluate the impact of synthetic data augmentation on diagnostic accuracy.DesignRetrospective chart review.Setting/Participan…

Оценка и анализ < < Биаса > > крупных языковых моделей для подготовки синтетических электронных медицинских записей: сравнительное исследование.

СПРАВОЧНАЯ ИНФОРМАЦИЯ: Синтетические электронные медицинские записи (ЭПЧ), генерируемые крупными языковыми моделями (ЛММ), открывают возможности для клинического обучения и типовой подготовки при одновременном решении проблем, связанных с неприкосновенностью частной жизни. Однако различия в показателях деятельности и демографические отклонения в этих моделях по-прежнему недостаточно изучены, что создает угрозу для справедливого медицинского обслуживания. ЦЕЛЬ: Данное исследование было направлено на систематическую оценку эффективности различных МУП в создании синтетических ЭПЧ и критическую оц

Совпадение в аннотации оригинала
BACKGROUND: Synthetic electronic health records (EHRs) generated by large language models (LLMs) offer potential for clinical education and model training while addressing privacy concerns. However, performance variations and demographic biases in these models remain underexplored, posing risks to equitable health care. OBJECTIVE: This study aimed to system…

Large language models know how the personality of public figures is perceived by the general public.

We show that people's perceptions of public figures' personalities can be accurately predicted from their names' location in GPT-3's semantic space. We collected Big Five personality perceptions of 226 public figures from 600 human raters. Cross-validated linear regression was used to predict human perceptions from public figures' name embeddings extracted from GPT-3. The models' accuracy ranged from r = .78 to .88 without controls and from r = .53 to .70 when controlling for public figures' likability and demographics, after correcting for attenuation. Prediction models showed high face valid

Retrieval-Augmented Generation for Large Language Models in Radiology: Another Leap Forward in Board Examination Performance.

Supplemental material is available for this article. See also the editorial by Forghani in this issue.

Lingdan: enhancing encoding of traditional Chinese medicine knowledge for clinical reasoning tasks with large language models.

OBJECTIVE: The recent surge in large language models (LLMs) across various fields has yet to be fully realized in traditional Chinese medicine (TCM). This study aims to bridge this gap by developing a large language model tailored to TCM knowledge, enhancing its performance and accuracy in clinical reasoning tasks such as diagnosis, treatment, and prescription recommendations. MATERIALS AND METHODS: This study harnessed a wide array of TCM data resources, including TCM ancient books, textbooks, and clinical data, to create 3 key datasets: the TCM Pre-trained Dataset, the Traditional Chinese P…

Identifying incarceration status in the electronic health record using large language models in emergency department settings.

BACKGROUND: Incarceration is a significant social determinant of health, contributing to high morbidity, mortality, and racialized health inequities. However, incarceration status is largely invisible to health services research due to inadequate clinical electronic health record (EHR) capture. This study aims to develop, train, and validate natural language processing (NLP) techniques to more effectively identify incarceration status in the EHR. METHODS: The study population consisted of adult patients (≥ 18 y.o.) who presented to the emergency department between June 2013 and August 2021. T

Utilizing Large Language Models for Enhanced Clinical Trial Matching: A Study on Automation in Patient Screening.

Background Clinical trial matching, essential for advancing medical research, involves detailed screening of potential participants to ensure alignment with specific trial requirements. Research staff face challenges due to the high volume of eligible patients and the complexity of varying eligibility criteria. The traditional manual process, both time-consuming and error-prone, often leads to missed opportunities. Recently, large language models (LLMs), specifically generative pre-trained transformers (GPTs), have become impressive and impactful tools. Utilizing such tools from artificial int…

Evaluating the Efficacy of Large Language Models in CPT Coding for Craniofacial Surgery: A Comparative Analysis.

BACKGROUND: The advent of Large Language Models (LLMs) like ChatGPT has introduced significant advancements in various surgical disciplines. These developments have led to an increased interest in the utilization of LLMs for Current Procedural Terminology (CPT) coding in surgery. With CPT coding being a complex and time-consuming process, often exacerbated by the scarcity of professional coders, there is a pressing need for innovative solutions to enhance coding efficiency and accuracy. METHODS: This observational study evaluated the effectiveness of 5 publicly available large language models…

Large language models assisted multi-effect variants mining on cerebral cavernous malformation familial whole genome sequencing.

Cerebral cavernous malformation (CCM) is a polygenic disease with intricate genetic interactions contributing to quantitative pathogenesis across multiple factors. The principal pathogenic genes of CCM, specifically KRIT1, CCM2, and PDCD10, have been reported, accompanied by a growing wealth of genetic data related to mutations. Furthermore, numerous other molecules associated with CCM have been unearthed. However, tackling such massive volumes of unstructured data remains challenging until the advent of advanced large language models. In this study, we developed an automated analytical pipeli…

Large language models for accurate disease detection in electronic health records: the examples of crystal arthropathies.

OBJECTIVES: We propose and test a framework to detect disease diagnosis using a recent large language model (LLM), Meta's Llama-3-8B, on French-language electronic health record (EHR) documents. Specifically, it focuses on detecting gout ('goutte' in French), a ubiquitous French term that has multiple meanings beyond the disease. The study compares the performance of the LLM-based framework with traditional natural language processing techniques and tests its dependence on the parameter used. METHODS: The framework was developed using a training and testing set of 700 paragraphs assessing 'go

Clinical Sentiment Analysis by Large Language Models Enhances the Prediction of Hepatorenal Syndrome in Decompensated Cirrhosis.

BACKGROUND AND AIMS: Hepatorenal syndrome - Acute Kidney Injury (HRS-AKI) is a severe complication of decompensated cirrhosis that is challenging to predict. Sentiment analysis, a computational process of identifying and categorizing opinions and judgment expressed in text, may enhance traditional prediction methodologies based on structured variables. Large language models (LLMs), such as generative pretrained transformers (GPTs), have demonstrated abilities to perform sentiment analyses on non-clinical texts. We sought to determine if GPT-performed sentiment analysis could improve upon predi…

Evaluating Large Language Models' Ability Using a Psychiatric Screening Tool Based on Metaphor and Sarcasm Scenarios.

Metaphors and sarcasm are precious fruits of our highly evolved social communication skills. However, children with the condition then known … ttention-deficit/hyperactivity disorder). This study employs a standardized test to evaluate recent large language models' (LLMs) understanding of nuanced human communication. The results indicate improved metaphor comprehension with increased model parameters; however, no similar improvement was observed for sarcasm comprehension. Considering that a human's ability to …