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Large language models in artificial intelligence to answer patient questions in spine surgery: an evaluation of current evidence.

BACKGROUND: Large language models (LLMs) are increasingly being explored in healthcare, particularly for enhancing patient education. In spine surgery, LLMs have the potential to enhance communication and support patients through perioperative care. However, concerns remain regarding the accuracy, readability, and overall reliability of these tools in delivering patient-facing information. This review aimed to understand the current use of LLMs in answering patient questions in spine surgery. METHODS: A structured search of PubMed and Google Scholar was conducted using terms focused on LLMs a…

Leveraging time-series electronic health records with large language models for chronic kidney disease diagnosis in primary care.

OBJECTIVES: Chronic kidney disease (CKD) presents a growing public health challenge in China, exacerbated by low patient awareness and limited nephrology resources. This study evaluated the potential of large language models (LLMs) to support CKD diagnosis in primary care using time-series electronic health record (EHR) data. METHODS: Longitudinal data were extracted from the EHR system of Weinan, China. Among 29 963 adults meeting inclusion criteria, 2300 participants were randomly sampled to generate clinical vignettes. CKD status was determined using diagnosis data, and individuals with ea…

Assessing the Accuracy of Large Language Models on European Guidelines for Cervical Cancer: An In Silico Benchmarking Study.

OBJECTIVE: Although large language models are increasingly used in clinical and research settings, the validity of the information they provide remains uncertain. This study aimed to evaluate the accuracy, consistency, and reliability of three large language models-ChatGPT 4.0, DeepSeek R1, and Gemini 2.0-in answering cervical cancer-related questions based on the ESGO/ESTRO/ESP guidelines. DESIGN: Prospective, comparative in silico benchmarking study. SETTING: Fondazione Policlinico Universitario A. Gemelli, Rome, Italy. POPULATION OR SAMPLE: Fifty questions derived from the ESGO/ESTRO/ESP…

Evaluation of large language models in patient education for hyperthyroidism: A comparative study of chatgpt, gemini, and deepseek.

PURPOSE: Hyperthyroidism is a common endocrine disorder that requires long-term management, heavily relying on effective patient education. This study aims to evaluate the application value of three mainstream large language models (LLMs) ChatGPT, Gemini, and DeepSeek in the education of hyperthyroidism patients. METHODS: We developed a standardized question bank containing 20 issues related to hyperthyroidism. The three LLMs were prompted to generate responses for each question. Five endocrinology experts performed a double-blind evaluation using a Likert 5-point scale across five dimensions:…

Role of Large Language Models for Suggesting Nerve Involvement in Upper Limbs MRI Reports with Muscle Denervation Signs.

OBJECTIVES: Determining the involvement of specific peripheral nerves (PNs) in the upper limb associated with signs of muscle denervation can be challenging. This study aims to develop, compare, and validate various large language models (LLMs) to automatically identify and establish potential relationships between denervated muscles and their corresponding PNs. MATERIALS AND METHODS: We collected 300 retrospective MRI reports in Spanish from upper limb examinations conducted between 2018 and 2024 that showed signs of muscle denervation. An expert radiologist manually annotated these reports …

Performance Comparison of Cutting-Edge Large Language Models on the ACR In-Training Examination: An Update for 2025.

PURPOSE: This study represents a continuation of prior work by Payne et al. evaluating large language model (LLM) performance on radiology board-style assessments, specifically the ACR diagnostic radiology in-training examination (DXIT). Building upon earlier findings with GPT-4, we assess the performance of newer, cutting-edge models, such as GPT-4o, GPT-o1, GPT-o3, Claude, Gemini, and Grok on standardized DXIT questions. In addition to overall performance, we compare model accuracy on text-based versus image-based questions to assess multi-modal reasoning capabilities. As a secondary aim, we

Optimising large language models for clinical information extraction: a benchmarking study in the context of ulcerative colitis research.

OBJECTIVE: Closed-source large language models (LLMs) like generative pre-trained transformer 4o (GPT-4o) have shown promise for clinical information extraction but are potentially limited by cost, data security concerns and inflexibility. Open-source models are an attractive alternative with various adaptation strategies with no consensus on best practices. This study aims to rigorously identify optimal adaptation strategies for open-source models and evaluate their performance relative to closed-source alternatives. METHODS AND ANALYSIS: We studied three LLM adaptation strategies: chain-of-…

Leveraging knowledge graphs and large language models for integrating molecular variants and clinical insights in COVID-19 research.

The relentless emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants continues to challenge global health, as h … , we systematically mined 439,724 coronavirus disease 2019 (COVID-19) publications using fine-tuned large language models to extract and distill knowledge across nine domains: antibodies, vaccines, serology, biochemistry, therapeutics, clinical presentation, risk factors, biomarkers, and diagnostics. These insights were integrated into a unified graph of 1,427,596 trip …

Evaluating Medium Scale, Open-Source Large Language Models: Towards Decision Support in a Precision Oncology Care Delivery Context.

INTRODUCTION: In the context of precision oncology, patients often have complex conditions that require treatment based on specific and up-to-date knowledge of guidelines and research. This entails considerable effort when preparing such cases for molecular tumor boards (MTBs). Large language models (LLMs) could help to lower this burden if they could provide such information quickly and precisely on demand. Since out-of-the-box LLMs are not specialized for clinical contexts, this work aims to investigate their usefulness for answering questions arising during MTB preparation. As such question…

АИ для научно обоснованной рекомендации в отношении лечения онкологии: ослепленная оценка крупных языковых моделей и агентских рабочих процессов.

Медицина, основанная на доказательствах, имеет решающее значение для принятия клинических решений, однако исследования свидетельствуют о том, что значительная часть решений о лечении не в полной мере учитывает последние данные. Большие языковые модели (ММЛ) говорят о том, что они способны устранить этот пробел, однако их надежность для медицинских рекомендаций остается неопределенной. five Рекомендации МЛМ в различных областях 50 клинические сценарии, связанные с множественным диагностированием миэломы, постановкой, лечением и управлением, с использованием унифицированного лимита улик по состо

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BackgroundEvidence-based medicine is crucial for clinical decision-making, yet studies suggest that a significant proportion of treatment decisions do not fully incorporate the latest evidence. Large Language Models (LLMs) show promise in bridging this gap, but their reliability for medical recommendations remains uncertain.MethodsWe conducted an evaluation …

Evaluating and leveraging large language models in clinical pharmacology and therapeutics assessment: From exam takers to exam shapers.

AIMS: In medical education, the ability of large language models (LLMs) to match human performance raises questions about their potential as educational tools. This study evaluates LLMs' performance on Clinical Pharmacology and Therapeutics (CPT) exams, comparing their results to medical students and exploring their ability to identify poorly formulated multiple-choice questions (MCQs). METHODS: ChatGPT-4 Omni, Gemini Advanced, Le Chat and DeepSeek R1 were tested on local CPT exams (third year of bachelor's degree, first/second year of master's degree) and the European Prescribing Exam (EuroP…

Enhanced Artificial Intelligence in Bladder Cancer Management: A Comparative Analysis and Optimization Study of Multiple Large Language Models.

Background: With the rapid advancement of artificial intelligence in health care, large language models (LLMs) demonstrate increasing potential in medical applications. However, their performance in specialized oncology remains limited. This study evaluates the performance of multiple leading LLMs in addressing clinical inquiries related to bladder cancer (BLCA) and demonstrates how strategic optimization can overcome these limitations. Methods: We developed a comprehensive set of 100 clinical questions based on established guidelines. These questions encompassed epidemiology, diagnosis, treat…

Towards Community-Based Evaluation of AI in Neurology: Development of a Headache Diagnosis Dataset for Large Language Models.

Diagnosing headache disorders remains a clinical challenge due to the heterogeneity of headache phenotypes and the absence of objective biomarkers. This study presents a curated dataset of 50 clinical headache case examples, comprising both real (n = 34) and synthetic (n = 16) cases, categorized across 20 diagnoses according to ICHD-3 criteria. The dataset enables the evaluation of large language models (LLMs) for diagnostic accuracy in headache medicine. Three GPT-based models were tested using different prompting strategies, with diagnostic performance assessed at both diagnosis and group le…

Evaluating Large Language Models for Sentiment Analysis and Hesitancy Analysis on Vaccine Posts From Social Media: Qualitative Study.

BACKGROUND: In the digital age, social media has become a crucial platform for public discourse on diverse health-related topics, including vaccines. Efficient sentiment analysis and hesitancy detection are essential for understanding public opinions and concerns. Large language models (LLMs) offer advanced capabilities for processing complex linguistic patterns, potentially providing valuable insights into vaccine-related discourse. OBJECTIVE: This study aims to evaluate the performance of various LLMs in sentiment analysis and hesitancy detection related to vaccine discussions on social med…

Automated Category and Trend Analysis of Scientific Articles on Ophthalmology Using Large Language Models: Development and Usability Study.

BACKGROUND: In this paper, we present an automated method for article classification, leveraging the power of large language models (LLMs). OBJECTIVE: The aim of this study is to evaluate the applicability of various LLMs based on textual content of scientific ophthalmology papers. METHODS: We developed a model based on natural language processing techniques, including advanced LLMs, to process and analyze the textual content of scientific papers. Specifically, we used zero-shot learning LLMs and compared Bidirectional and Auto-Regressive Transformers (BART) and its variants with Bidirection…

Performance and Reproducibility of Large Language Models in Named Entity Recognition: Considerations for the Use in Controlled Environments.

INTRODUCTION: Recent artificial intelligence (AI) advances can generate human-like responses to a wide range of queries, making them a useful tool for healthcare applications. Therefore, the potential use of large language models (LLMs) in controlled environments regarding efficacy, reproducibility, and operability will be of paramount interest. OBJECTIVE: We investigated if and how GPT 3.5 and GPT 4 models can be directly used as a part of a GxP validated system and compared the performance of externally hosted GPT 3.5 and GPT 4 against LLMs, which can be hosted internally. We explored zero-…

Man vs the machine in the struggle for effective text anonymisation in the age of large language models.

The collection and use of personal data are becoming more common in today's data-driven culture. While there are many advantages to this, in … e them. The latter allows us to revise these methods and introduce a novel methodology that employs Large Language Models to improve the anonymity of texts.

[pMHCBind: predicting major histocompatibility complex class I-peptide interactions via protein large language models and hypergraph neural networks].

Major histocompatibility complex class I (MHC I) molecules initiate CD8+ T cell immune responses by presenting antigenic peptides, a process fundamentally governed by the spatial interaction network mediated by multi-residue synergy between the binding groove and the peptide. Accurate modeling of this mechanism is crucial for tumor neoantigen screening and personalized immunotherapy. However, existing methods are largely confined to sequence-level modeling, failing to capture cross-residue higher-order structural dependencies and fine-grained binding interface interactions. To address these ch

Relationship extraction for adverse drug events in clinical notes using large language models: a retrospective cross-sectional study.

BACKGROUND: Adverse drug events (ADEs) are a critical indicator of patient safety but are often documented only in free-text clinical notes. The potential of recent advances in natural language processing (NLP), particularly generative large language models (LLMs), to identify ADEs remains understudied. This study aimed to compare the performance of multiple LLMs in identifying ADE-Drug relationships in inpatient and ambulatory clinical notes. METHODS: We used clinical notes from the 2018 National NLP Clinical Challenge (n2c2) ADE dataset (inpatient; n = 505) and from outpatient encounters (n…