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HAIant (): A human-centered AI framework for secure, personalized intelligence augmentation in biological research.

Artificial intelligence (AI) has achieved human-level capabilities across multiple domains; however, concerns regarding data security, privacy, and domain-specific capability enhancement remain. Here, we present human + AI + ant (HAIant), a human-centered framework for secure and personalized intelligence augmentation based on locally deployed AI. HAIant integrates locally deployed large language models with personal data, individual reasoning processes, and domain-specific tools, enabling user-controlled AI systems while preserving data privacy. Within this framework, HAIant establishes a uni…

Quantum nonlinearity for optical neural computing.

The rapid scaling of deep neural networks comes at the cost of unsustainable power consumption. While optical neural networks offer an alter … s of magnitude below the nonlinearity threshold of conventional optical materials. Looking ahead to large language models, we estimate the nonlinearity-limited optical power, which scales sublinearly with model size. Our results indicate that quantum nanophotonics may provide a route toward sustainable AI inference.

Data-Driven, Mechanistically Guided Prediction of Yield and Chemoselectivity in SuFEx Reactions.

Sulfur(VI) fluoride exchange (SuFEx) has emerged as a powerful click reaction for constructing diverse S(VI)-based linkages across chemical biology, materials science, and drug discovery. However, the diversity of SuFEx hubs and native nucleophiles generates a high-dimensional reaction landscape in which yields and chemoselectivities are not always straightforward to anticipate, limiting its predictability in synthetic planning. Here, we demonstrate that both SuFEx yield and chemoselectivity can be rendered predictable by integrating machine learning with curated reaction data and mechanistic

OntoCodex: a multi-agent biomedical ontology enrichment framework.

Ontology enrichment is a critical but labor-intensive step in semantic knowledge representation. To address this challenge, we propose OntoCodex, a multi-agent framework that integrates large language models (LLMs), ontologies, curated knowledge sources, and standard vocabularies to support semi-automated ontology enrichment with formal OWL-based integration and a feedback loop. OntoCodex consists of five coordinated agents for ontology parsing, task decision-making, knowledge retrieval, terminology normalization, and automated script generation. We evaluated OntoCodex using a ChatGPT-4o-power…

Artificial intelligence in pituitary surgery: the path to clinical solutions.

Artificial intelligence (AI) is rapidly moving from conceptual innovation to high-performing algorithms across the pituitary patient pathway, promising benefits to patients, endocrinologists and surgeons alike. Pre-operatively, machine learning applied to facial imaging and natural language processing of electronic health records show potential for earlier identification of pituitary adenomas and timelier referral from primary providers to a specialist endocrinologist. Intraoperatively, computer vision systems have already augmented surgical training but have the potential to become integrated

ChunkUIE: Chunked instruction-based unified information extraction.

Large language models (LLMs) have demonstrated remarkable performance across various linguistic tasks. However, existing LLMs perform inadequately in information extraction tasks for both Chinese and English. Numerous studies attempt to enhance model performance by increasing the scale of training data. However, discrepancies in the number and type of schemas used during training and evaluation can harm model effectiveness. To tackle this challenge, we propose ChunkUIE, a unified information extraction model that supports Chinese and English. We design a chunked instruction construction strate…

Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge.

Many real-world image recognition problems, such as diagnostic medical imaging exams, are "long-tailed" - there are a few common findings followed by many more relatively rare conditions. In chest radiography, diagnosis is both a long-tailed and multi-label problem, as patients often present with multiple findings simultaneously. While researchers have begun to study the problem of long-tailed learning in medical image recognition, few have studied the interaction of label imbalance and label co-occurrence posed by long-tailed, multi-label disease classification. To engage with the research co

Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge.

Many real-world image recognition problems, such as diagnostic medical imaging exams, are "long-tailed" - there are a few common findings followed by many more relatively rare conditions. In chest radiography, diagnosis is both a long-tailed and multi-label problem, as patients often present with multiple findings simultaneously. While researchers have begun to study the problem of long-tailed learning in medical image recognition, few have studied the interaction of label imbalance and label co-occurrence posed by long-tailed, multi-label disease classification. To engage with the research co

Application of Artificial Intelligence in the Headache Field.

PURPOSE OF REVIEW: Headache disorders are highly prevalent worldwide. Rapidly advancing capabilities in artificial intelligence (AI) have expanded headache-related research with the potential to solve unmet needs in the headache field. We provide an overview of AI in headache research in this article. RECENT FINDINGS: We briefly introduce machine learning models and commonly used evaluation metrics. We then review studies that have utilized AI in the field to advance diagnostic accuracy and classification, predict treatment responses, gather insights from various data sources, and forecast mi

Varieties of developmental dyslexia.

This paper reviews and evaluates the evidence for the existence of distinct varieties of developmental dyslexia, analogous to those found in the acquired dyslexic population. Models of the normal adult reading process and of the development of reading in children are used to provide a framework for considering the issues. Data from a large-sample study of the reading patterns of developmental dyslexics are then reported. The lexical and sublexical reading skills of 56 developmental dyslexics were assessed through close comparison with the skills of 56 normally developing readers. The results i

Artificial intelligence to support clinical practice across the inpatient heart failure care pathway: a systematic review.

Heart failure is a complex clinical syndrome requiring repeated reassessment, close monitoring, and timely treatment decisions throughout ho … on, and medication adherence prediction, using electronic health record-derived data. At discharge, large language models and digital tools mainly supported patient education and self-management, although limited actionability and implementation barriers remained. Artificial intelligence may support inpatient heart failure care, but evidence of measurable clinical bene …

Evaluating the Utility of Artificial Intelligence in Conducting Systematic Reviews.

BACKGROUND: Systematic reviews and meta-analyses represent the highest level of evidence in clinical research, but the process of article retrieval and screening is labor-intensive. Large language models, such as ChatGPT-5, may offer an efficient alternative, yet their performance in full systematic review workflows remains untested. This study compares ChatGPT-5's Deep Research and Agent Modes with human researchers in replicating gold standard systematic reviews in total joint arthroplasty. METHODS: Five published systematic reviews were selected as reference articles. Three groups: orthopa…

From Algorithms to Clinics: Recent Progress in AI for Scoliosis Diagnosis and Management.

Scoliosis is a complex three-dimensional spinal deformity that requires early detection, accurate radiological assessment, individualized tr … integrating radiological, clinical, biomechanical, and, increasingly, multimodal data. In parallel, large language models and generative AI tools are being investigated for patient education, communication support, readability improvement, and research hypothesis generation. Despite these advances, important limitations remain, including limited external validation, da …

Evaluating the Role of AI Assistants in Accelerating Neurodegenerative Disease Research: Opportunities and Translational Limitations.

Neurodegenerative diseases including Alzheimer's disease and Parkinson's disease remain among the most challenging disorders to study, diagn … diction, drug target prioritization, and clinical data mining. More recently, foundation models and large language models (LLMs) have expanded AI from task-specific prediction tools to versatile assistants supporting literature retrieval, summarization, coding, data interpretation, and hypothesis generation. Although AI assistants promise to accelerate research workflo …

От прогнозирования к вмешательству: Искусственный интеллект для адаптивного реагирования и моделирования токсичности в клеточной терапии для гематологовых злодеяний.

Гематологическая магнелезность, включая острую лейкемию миэлоида, миэлодипластические синдромы, лимфому и множественную миелому, характеризуется глубокой биологической гетерогенностью и высокодинамичными траекториями лечения, которые традиционные статические прогнозирующие системы неполно улавливают. Клеточная терапия, такая, как химерик антигенная рецепторная терапия Т-клеток и трансплантация стволовых клеток с гематополией, предлагает потенциально лечебные варианты для рецидива или рефракторного заболевания, однако результаты остаются весьма различными, а управленческие решения, касающиеся и

Совпадение в аннотации оригинала
Hematologic malignancies, including acute myeloid leukemia, myelodysplastic syndromes, lymphoma, and multiple myeloma, are characterized by … l integrative frameworks, reinforcement learning, digital twins, and emerging foundation models and large language models; and proposes an adaptive, multimodal paradigm. We examine key enabling technologies and address the clinical, regulatory, ethical, and implementation challenges that must be resolved before these systems can be deployed at the bedside. We argue tha …

LLM-Enhanced Multimodal Fusion of SPECT Radiomics and Clinical Data for Predicting 131I Therapeutic Response in Differentiated Thyroid Cancer.

BACKGROUND: Patients with differentiated thyroid cancer (DTC) treated with radioactive iodine (RAI) remain at risk of adverse outcomes. Early prediction of RAI efficacy is critical for optimizing clinical management. PURPOSE: To develop a model integrating Single Photon Emission Computed Tomography (SPECT) radiomic features and clinical variables for predicting the therapeutic efficacy of 131I treatment in patients with DTC. MATERIALS AND METHOD: The SPECT images and clinical data from 311 patients were included in this study. A total of 1,688 radiomic features were extracted from SPECT imag

Assessing AI Accuracy in Generating CPT Codes From Surgical Operative Notes.

INTRODUCTION: Accurate and efficient medical coding is essential for proper reimbursement and health care management. Current Procedural Terminology (CPT) codes, derived from operative notes, standardize medical billing but are often prone to variability and errors due to the complexity of surgical procedures. With advancements in artificial intelligence (AI), tools like ChatGPT and other large language models (LLMs) are being explored for their potential to automate coding tasks. This study evaluates the ability of LLMs to generate accurate CPT codes for craniofacial surgical procedures based…

Artificial intelligence performance in answering multiple-choice oral pathology questions: a comparative analysis.

BACKGROUND: Artificial intelligence (AI) has rapidly advanced in healthcare and dental education, significantly impacting diagnostic processes, treatment planning, and academic training. The aim of this study is to evaluate the performance differences between different large language models (LLMs) by analyzing their accuracy rates in answers to multiple choice oral pathology questions. METHODS: This study evaluates the performance of eight LLMs (Gemini 1.5, Gemini 2, ChatGPT 4o, ChatGPT 4, ChatGPT o1, Copilot, Claude 3.5, Deepseek) in answering multiple-choice oral pathology questions from th…

Utility of OpenEvidence and ChatGPT in Selecting Neuroimaging Based on Appropriateness Criteria.

RATIONALE AND OBJECTIVES: Large Language models (LLMs) are novel tools with promising applications in imaging selection for clinical scenarios, but information is limited on the accuracy of an LLM trained on a medically specific dataset compared to a generalized dataset. In this analysis, we assessed the accuracy of the imaging recommendations by LLMs ChatGPT-3.5, ChatGPT-5.0, and OpenEvidence for neuroradiology, using the American College of Radiology (ACR) Appropriateness Criteria as the reference standard. MATERIALS AND METHODS: Prompts from the ACR Appropriateness Criteria priority clinic…

Artificial Intelligence in Mass Spectrometry-Based Metabolomics and Lipidomics for Drug Discovery and Development.

Artificial intelligence applied to mass spectrometry-based metabolomics and lipidomics has advanced through three technological generations: chemometric and classical machine learning, deep learning, and self-supervised foundation models, with large language models so far entering mainly at the edges. The metabolome and lipidome lie closest to drug action, yet they have been among the slowest omics layers to adopt modern artificial intelligence. This paradox arises from their downstream position, which leaves them without a closed molecular reference and without the standardized, readily model…