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VaxKG: Integrating The Vaccine Ontology And VIOLIN For Advanced Vaccine Queries And LLM-Powered Chat Systems.

Vaccine research faces challenges in integrating diverse biomedical datasets. While the Vaccine Investigation and Online Information Network (VIOLIN) provides comprehensive vaccine data, implemented in traditional relational models limit complex analysis. Similarly, the Vaccine Ontology (VO) offers standardized semantic frameworks but lacks comprehensive empirical data. This study addresses these limitations by developing the Vaccine Knowledge Graph (VaxKG) that integrates VIOLIN's dataset with VO's standardized terminology. Using Neo4j, we transformed 12 core VIOLIN tables into a graph struct

Diagnosis, treatment, and prevention of ankle sprains: Comparing free chatbot recommendations with clinical guidelines.

BACKGROUND: Free chatbots powered by large language models offer lateral ankle sprains (LAS) treatment recommendations but lack scientific validation. METHODS: The chatbots-Claude, Perplexity, and ChatGPT-were evaluated by comparing their responses to a questionnaire and their treatment algorithms against current clinical guidelines. Responses were graded on accuracy, conclusiveness, supplementary information, and incompleteness, and evaluated individually and collectively, with a 60 % pass threshold. RESULTS: The collective analysis of the questionnaire showed Perplexity scored significantl…

The ansa subclavia: a review of the literature.

The ansa subclavia, subclavian loop, Vieussens' ansa or Vieussens' loop is a nerve cord that connects the middle cervical and inferior cervical sympathetic ganglia, forming a loop around the subclavian artery. The structure of the ansa subclavia is evolutionarily conserved from rats, guinea pigs, the porcine species and dogs to humans. A common application in physiological studies is to electrically stimulate the ansa subclavia in animal models as a robust protocol to modulate stimulatory cardiac sympathetic input. Despite a large number of physiological studies utilizing the ansa subclavia, o

Emerging artificial intelligence advances in oncology: latest updates from the 2026 AACR annual meeting.

Advances presented at the 2026 American Association for Cancer Research (AACR) Annual Meeting highlight a shift from standalone artificial intelligence (AI) models to integrated, agentic systems across oncology. Platforms such as Synapse enable large-scale data coordination, supporting interoperable and reproducible research. Building on this foundation, conversational and multi-agent AI tools (e.g., DrBioRight, GP CoPilot, Isabl AI Agent) allow natural language interaction with multimodal cancer data, lowering technical barriers. Agentic frameworks for real-world data (RWD) transformation, in

[Simulated consciousness, pseudo-empathy and the illusion of attachment: what can psychiatry do with artificial intelligence?].

Over the past decade, the burden of mental disorders has grown while services remain capacity-constrained, pushing generative artificial intelligence toward psychiatric practice. This narrative review outlines how large language and multimodal models work and where they fail (hallucinations, goal drift, model drift), then appraises the evidence across diagnosis, therapy, and ethical-social implications. We review decision support based on digital phenotyping, clinical text, speech analysis, and wearable sensors, and summarize short-term findings and limitations of protocol-based and generative

Curation and Extraction of Drug-Related Entities from Reddit Platform.

Physicians learn primarily about illicit drugs from clinical overdose cases, limiting their understanding of real-world usage. Meanwhile, drug users share first-hand experiences online, offering insights into dosage and effects of drugs. To bridge this gap, we introduce ReDose (REddit Drug DOSe and Effect), a dataset of 6,435 Reddit posts on substance use. A board-certified toxicologist primarily annotated both the training and test sets, while two medical science students contributed to the test set, labeling DRUG, DOSE, and EFFECT entities. We benchmarked 6,267 annotations using BERT-based,

Transforming perioperative care: The current landscape and future trajectory of artificial intelligence in anesthesia-A narrative review.

This narrative review examined how artificial intelligence is increasingly being applied in anesthesiology to support clinical decision-making across the perioperative period. It outlines current applications of artificial intelligence in preoperative risk assessment, intraoperative monitoring and automation, and postoperative complication prediction. We also examined the underlying artificial intelligence architectures that form the technical foundations of these tools, including machine learning, deep learning, and natural language processing. We propose that in the future, rather than narro

LLMs as emerging tools for understanding and managing bone metastasis and cancer-induced bone pain.

Artificial intelligence (AI) chatbots powered by large language models (LLMs) show promise in human-like responses, yet their utility for complex biomedical issues remains uncertain. We evaluated four leading LLMs, including ChatGPT-5.2, Claude 4.5 Sonnet, Gemini 3.0 Pro, and DeepSeek-R1, on their performance in addressing queries related to bone metastasis (BM) and cancer-induced bone pain (CIBP). Using 18 structured questions derived from bibliometric analysis, we assessed responses across research, clinical, and patient perspectives. Qualitatively rating based on accuracy, readability, comp…

The Human Flourishing Geographic Index: A County-Level Dataset for the United States, 2013-2023.

Quantifying human flourishing-a multidimensional construct including happiness, health, purpose, virtue, relationships, and financial stability-is critical for understanding societal well-being beyond economic indicators. Existing measures often lack fine spatial and temporal resolution. Here we introduce the Human Flourishing Geographic Index (HFGI), derived from analyzing approximately 2.6 billion geolocated U.S. tweets (2013-2023) using fine-tuned large language models to classify expressions across 48 indicators aligned with Harvard's Global Flourishing Study framework plus attitudes towar…

Current progress and open challenges for applying artificial intelligence across the in vitro fertilization cycle.

In vitro fertilization (IVF) has significantly advanced the treatment of infertility, yet success rates remain modest due to its complexity … progress. We also examine the potential of emerging AI approaches, such as multi-modal learning and large language models. In addition, we acknowledge ongoing challenges, including limited model generalizability, data bias, and the need for clinically validated, transparent AI systems. While the integration of AI into IVF is promising, its success will depend on rigoro …

Mixing Expert Knowledge with LLM to Improve Dialogue in Serious Games for Anorexia Caregivers: A Feasibility Study.

Anorexia Nervosa (AN) is a severe eating disorder requiring interventions that effectively involve the entire family. Family-Based Therapy (FBT) has shown promise, yet traditional training methods for caregivers can be time-consuming and challenging. We present a serious game leveraging large language models (LLMs) that generate realistic, scenario-based dialogues between caregivers and teens with AN, offering a safe space to practice supportive communication strategies. To enhance the credibility and therapeutic relevance of these generated dialogues, we utilize a few-shot learning approach i…

First steps toward building natural history of diseases computationally: Lessons learned from the Noonan syndrome use case.

Rare diseases (RDs) are conditions affecting fewer than 1 in 2,000 people, with over 7,000 identified, primarily genetic in nature, and more than half impacting children. Although each RD affects a small population, collectively, between 3.5% and 5.9% of the global population, or 262.9-446.2 million people, live with an RD. Most RDs lack established treatment protocols, highlighting the need for proper care pathways addressing prognosis, diagnosis, and management. Advances in generative AI and large language models (LLMs) offer new opportunities to document the temporal progression of phenotyp…

PRIME 2.0: Proposed Requirements for Cardiovascular Imaging-Related Multimodal-AI Evaluation: An Updated Checklist.

The PRIME (Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation) 2.0 checklist is an updated, domain-specific framework designed to standardize the development, evaluation, and reporting of artificial intelligence (AI) applications in cardiovascular imaging. This update specifically responds to rapid advances from traditional machine learning to deep learning, large language models, and multimodal generative AI. The updated checklist was developed through a modified Delphi process by an international panel of clinical and technical experts. In contrast to genera…

Automating sedation state assessments using natural language processing.

INTRODUCTION: Common goals for procedural sedation are to control pain and ensure the patient is not moving to an extent that is impeding safe progress or completion of the procedure. Clinicians perform regular assessments of the adequacy of procedural sedation in accordance with these goals to inform their decision-making around sedation titration and also for documentation of the care provided. Natural language processing could be applied to real-time transcriptions of audio recordings made during procedures in order to classify sedation states that involve movement and pain, which could the

Consistency and accuracy of different artificial intelligence models in evaluating longitudinal cracks and fractures of teeth.

Large language models such as ChatGPT and Google Gemini have attracted increasing attention in dentistry due to their potential to support clinical decision-making; however, their reliability in responding to endodontic questions remains unclear. This study compared the accuracy, consistency, and temporal stability of three artificial intelligence models (ChatGPT-3.5, ChatGPT-4o, and Google Gemini) when answering dichotomous clinical questions on vertical root fractures and tooth cracks using expert responses as the reference standard. Seventy-two true/false questions were developed based on t…

Towards efficient and reliable artificial intelligence through neuromorphic principles.

Artificial intelligence (AI) research today is largely driven by ever-larger neural network models trained on graphics processing units (GPU … eliable in the sense that they cannot properly quantify uncertainty in their decisions-for example, large language models (LLMs) tend to produce incorrect outputs with high confidence. This paper argues that achieving more efficient and reliable AI will require embracing a set of principles that are well aligned with the goals of neuromorphic engineering, which are in …

Readability, Accuracy, and Lexical Diversity of New ChatGPT Models for Common Carpal Tunnel Syndrome Questions.

BACKGROUND: Carpal tunnel syndrome (CTS) is a prevalent neuropathy in hand surgery that significantly affects people's quality of life. Frequently, patients conduct research online before seeking medical care. Large language models (LLMs) like ChatGPT are increasingly used for health information, yet concerns remain regarding the accuracy, readability, and complexity of their responses. Previous studies have assessed older ChatGPT models but have not comprehensively compared newer versions. The purpose of this study is to compare ChatGPT-4-generated, ChatGPT-4o-generated, and ChatGPT-o1-genera…

Feasibility, Enjoyment, and Language Comprehension Impact of a Tablet- and GameFlow-Based Story-Listening Game for Kindergarteners: Methodological and Mixed Methods Study.

BACKGROUND: Enjoyment plays a key role in the success and feasibility of serious gaming interventions. Unenjoyable games will not be played, and in the case of serious gaming, learning will not occur. Therefore, a so-called GameFlow model has been developed, which intends to guide (serious) game developers in the process of creating and evaluating enjoyment in digital (serious) games. Regarding language learning, a variety of serious games targeting specific language components exist in the market, albeit often without available assessments of enjoyment or feasibility. OBJECTIVE: This study e

агитационные рамки ИИ, связывающие языковые модели с электронными медицинскими записями, и график биомедицинских знаний для реальных доказательств.

Доступ к крупномасштабным клиническим и биомедицинским базам данных по-прежнему является серьезным препятствием для клинических и научных работников, требующим значительных вычислительных знаний. Агентские системы искусственного интеллекта, в которых крупные языковые модели (МПМ) организуют многоэтапное рассмотрение и выполнение запросов под интерактивным контролем человека, открывают возможности для демократизации доступа к данным и ускорения процесса получения доказательств. MethodsWe применяли Типовой контекстный протокол (МКП) для интеграции в рамках одного агентского рабочего процесса ста

Совпадение в аннотации оригинала
BackgroundAccessing large-scale clinical and biomedical databases remains a significant barrier for clinicians and researchers, requiring substantial computational expertise. Agentic artificial intelligence frameworks, in which large language models (LLMs) orchestrate multi-step reasoning and query execution under interactive human supervision, offer the pot…

KPTM-MCHR: A multichannel hypergraph regression model for predicting the frequencies of lysine post-translational modification types in proteins.

Lysine post-translational modifications (K-PTMs) are important forms of protein post-translational modification. Identifying K-PTMs in protein sequences is an important topic in bioinformatics. Existing computational models mainly focus on predicting specific K-PTM types at lysine residues in protein sequences. Few studies have investigated the number of K-PTM types that can be annotated to a given protein sequence. In this study, we present a pioneering model for predicting this number, which we term the frequency of protein K-PTM types. Three hypergraphs are constructed from a comprehensive