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Experiences who are d/Deaf and queer individuals: a systematic review and meta-synthesis.

The current systematic review and meta-synthesis investigated the experiences of Deaf queer people, specifically, how identities in this population shift dynamically in response to their dual marginalization. Data were extracted from 27 qualitative studies, with a sample total of 176 participants. Synthesis was influenced by the Model of Multidimensional Identities to frame resulting categories and themes. Four categories were developed from the data: (1) institutional contributors to identity salience, (2) cultural moderators of institutional neglect, (3) resistance to double exclusion throug

AI as a Therapist, Companion, and Romantic Partner: Emerging Roles, Benefits, and Risks for Mental Health in Participatory Medicine.

The line between tool and companion was once obvious, but conversational AI is blurring it in ways few researchers anticipated. Large language model chatbots and purpose-built AI companion agents are now used by millions of people every day. They are not being used to simply retrieve information but, instead, to offer emotional support, help process personal distress, and sustain what many describe as genuine relationships. Research puts the scale of this shift in sharp relief as nearly half (48.7%) of individuals with self-reported mental health concerns report having used a large language mo

Evaluation of an AI-assisted digital health follow-up system integrating humanistic care for patients undergoing chemotherapy: a prospective quasi-experimental study.

BACKGROUND: Chemotherapy imposes substantial physical and psychological burdens that may reduce treatment adherence and quality of life. Traditional follow-up is often fragmented, reactive, and insufficiently personalized. Artificial intelligence (AI) may improve continuity, responsiveness, and patient-centered care; however, its integration with humanistic care remains underexplored. METHODS: This prospective, single-center, quasi-experimental study was conducted from January to June 2024. A total of 189 patients undergoing chemotherapy were allocated to an AI-assisted follow-up group (n = 9

ChatGPT in general surgery: a cross-sectional study assessing its response to patient questions.

BACKGROUND: The artificial intelligence-based large language model ChatGPT (OpenAI, San Francisco/CA, USA) has taken human-machine interaction to the next level since its launch in November 2022. As a program that mimics human conversations over a text-based communication interface, ChatGPT has the potential to be used in a variety of healthcare settings, including patient information. The present study aimed to assess the abilities of ChatGPT in responding to patient questions regarding general surgery. METHODS: A questionnaire-based cross-sectional study, comprising a total of 30 commonly a

Can AI-Based ChatGPT Models Accurately Analyze Hand-Wrist Radiographs? A Comparative Study.

Background/Aims: The aim of this study was to evaluate the effectiveness of large language model (LLM)-based chatbot systems in predicting bone age and identifying growth stages, and to explore their potential as practical, infrastructure-independent alternatives to conventional methods and convolutional neural network (CNN)-based deep learning models. Methods: This study evaluated the performance of three ChatGPT-based models (GPT-4o, GPT-o4-mini-high, and GPT-o1-pro) in predicting bone age and growth stage using 90 anonymized hand-wrist radiographs (30 from each growth stage-pre-peak, peak,

Testing Knowledge Boundaries: Adversarial Evaluation of LLMs for Antimicrobial Stewardship.

OBJECTIVES: Evaluate whether general-purpose large language models (LLMs) demonstrate competencies suitable for antimicrobial stewardship (AMS) support and characterize their failure modes. METHODS: Cross-sectional evaluation of seven LLMs (GPT-5, Claude Sonnet 4.5, Gemini 2.5 Pro, Grok 4, Llama-3.3-70b-instruct, Qwen 2.5-72b-instruct, DeepSeek-chat-v3.1) using 30 clinical scenarios mapped to ESCMID AMS competency frameworks. Scenarios included deliberate traps for fabrication and dangerous recommendations. Six AMS experts from the Netherlands and Spain performed blinded dual evaluation using…

Generative Artificial Intelligence to Automate the Adaptation of Excel Health Economic Models and Word Technical Reports.

OBJECTIVES: In health economics and outcomes research (HEOR), many repetitive tasks could be performed by large language models (LLMs), including adapting Excel-based health economic models and associated Word technical reports to a new setting. However, it is vital to develop robust methods so that the LLM delivers at least human-level accuracy. METHODS: We developed LLM-based pipelines to automate parameter value adaptations for Excel-based models and subsequent reporting of the model results. Chain-of-thought prompting, ensemble shuffling, and task decomposition were used to enhance the ac…

Emerging applications of traditional and generative artificial intelligence in liver transplantation.

Artificial intelligence (AI) has emerged as a transformative force in liver transplantation (LT), spanning patient selection, donor-recipient matching, intraoperative management, and post-transplant care. Historically, applications have relied on traditional machine learning (ML) and deep learning (DL) techniques trained on structured clinical datasets to improve risk stratification and outcome prediction. Since 2023, however, advances in generative AI (GenAI) and large language models (LLMs) have expanded the scope of AI in LT, enabling multimodal reasoning and the extraction of clinically me…

Artificial intelligence-driven prediction and design of cell-penetrating peptides for advanced drug delivery system.

BACKGROUND: Cell-penetrating peptides (CPPs) are promising delivery vectors for transporting therapeutic agents across cellular membranes. However, their rational design remains challenging because the relationship between peptide sequence and translocation efficiency is highly complex and nonlinear. METHODS: In this study, we developed an artificial intelligence-driven framework that integrates biochemical rules derived from large language models (LLMs) with conventional peptide descriptors for CPP prediction and design. Interpretable rules extracted from GPT-4o and DeepSeek were encoded as …

Generative Versus Nongenerative Artificial Intelligence.

Artificial intelligence (AI) is a colossal buzzword, a confusing subject matter, but also an inevitable reality. Generative and nongenerative AI are the 2 core subtypes of AI. Generative AI uses current data to understand patterns and generate new information, and it is especially valuable in producing synthetic medical images, enhancing surgical simulations, and expanding training datasets. Techniques such as generative adversarial networks (GANs), large language models (LLMs), and variational autoencoders (VAEs) allow for the creation of realistic simulations, text, and models that can be us…

Can machine learning predict PTSD symptoms from trauma narratives of children and adolescents?

Background: Machine learning approaches are being increasingly tested as a potential means of identifying mental health conditions. Narrativ … can predict PTSD symptoms in young individuals exposed to trauma.Study methodology: Two pre-trained large language models and two benchmark models were fine-tuned and trained to predict PTSD symptom severity from children's autobiographical narratives of a traumatic event. Data comprised narratives collected one month post-trauma from 400 individuals aged 7-17 years ol …

LLM-Guided Pain Management: Examining Socio-Demographic Gaps in Cancer vs non-Cancer cases.

Large language models (LLMs) offer potential benefits in clinical care. However, concerns remain regarding socio-demographic biases embedded in their outputs. Opioid prescribing is one domain in which these biases can have serious implications, especially given the ongoing opioid epidemic and the need to balance effective pain management with addiction risk. We tested ten LLMs-both open-access and closed-source-on 1,000 acute-pain vignettes. Half of the vignettes were labeled as non-cancer and half as cancer. Each vignette was presented in 34 socio-demographic variations, including a control g…

Comparative diagnostic accuracy of ChatGPT-4 and machine learning in differentiating spinal tuberculosis and spinal tumors.

BACKGROUND: In clinical practice, distinguishing between spinal tuberculosis (STB) and spinal tumors (ST) poses a significant diagnostic challenge. The application of AI-driven large language models (LLMs) shows great potential for improving the accuracy of this differential diagnosis. PURPOSE: To evaluate the performance of various machine learning models and ChatGPT-4 in distinguishing between STB and ST. STUDY DESIGN: A retrospective cohort study. PATIENT SAMPLE: A total of 143 STB cases and 153 ST cases admitted to Xiangya Hospital Central South University, from January 2016 to June 202…

От сегментации к объяснению: подготовка текстовых отчетов МРТ с ММ.

Искусственная разведка (АИ) значительно усовершенствовала медицинскую съемку, однако непрозрачность моделей глубокого обучения остается проблематичной, что часто снижает доверие медицинских специалистов к диагностике на основе АИ. В результате большое внимание уделяется повышению транспарентности и интерпретируемости моделей ИИ, с тем чтобы повысить доверие поставщиков медицинских услуг к этим технологиям. МЕТОДЫ: В настоящем документе предлагается новый подход к повышению степени объясняемости ИИ при выполнении важнейших медицинских задач путем интеграции современных моделей семантической сег

Совпадение в аннотации оригинала
BACKGROUND AND OBJECTIVE: Artificial Intelligence (AI) has significantly advanced medical imaging, yet the opacity of deep learning models r … cal tasks by integrating state-of-the-art semantic segmentation models with atlas-based mapping and Large Language Models (LLMs) to produce comprehensive, human-readable medical reports. The proposed framework ensures that the generated outputs are factual and contextually rich. Our anti-hallucination design, which combines structured JSON with prompt constraints, is a …

Comparative Assessment of Performance of Domain-Specific and Primed Versus Non-Primed Artificial Intelligence Chatbots for Clinical Decision-Making in Injuries to the Primary Dentition: An Exploratory Pilot Study.

BACKGROUND/AIMS: To compare the performance of a domain-specific dental trauma chatbot with general-purpose artificial intelligence chatbots … ls provide reliable management guidance, contextual exposure to clinical guidelines enables general large language models to demonstrate a high level of agreement with established recommendations in certain aspects of trauma care in primary dentition. These findings highlight the potential role of prompt-guided AI systems as supportive tools in dental traumatology deci …

Comparative Evaluation of ChatGPT and Gemini in Approximating Clinically Confirmed Diagnoses From Structured Numerical Pure-Tone Audiogram Data.

INTRODUCTION: The utilization of large language models (LLMs) in healthcare shows promising potential for addressing the global shortage of audiologists. However, their effectiveness in understanding and interpreting complex, live, real-world structured audiological data requires further investigation, as previous studies have primarily focused on theoretical situations and scenarios. This study aimed to assess and compare the diagnostic inference abilities of ChatGPT (OpenAI, San Francisco, USA) and Gemini (Google, Mountain View, USA) in differentiating clinically confirmed diagnoses based on…

Report Supervision.

Segmentation models can surpass radiologists, classification models, and vision-language models in tumor detection. Furthermore, segmentation models outline tumors, allowing radiologists to better verify and trust the AI output. However, their main limitation is the scarcity of tumor masks: creating one 3D tumor mask takes up to 30 min, so most public CT datasets contain only a few hundred masks, and even the largest private datasets contain only a couple of thousand. Tumor masks are not produced in clinical routine, but radiology reports are. Public datasets contain tens of thousands of CT-Re

PKGPT: Expert-Orchestrated Recursive LLM Agent for Automated NONMEM PopPK Modeling with Human Benchmarking.

Background/Objectives: Population pharmacokinetic (PopPK) modeling in NONMEM requires iterative, expertise-dependent workflows. Naïve zero-shot prompting of general-purpose large language models (LLMs) typically produces NONMEM code that fails to execute. This study introduces PKGPT, a recursive agentic LLM system designed to automate NONMEM-based PopPK model development and benchmarks its performance against human expert models. Methods: PKGPT, powered by Google's Gemini 3.0 Flash, embeds pharmacometrics expertise into phase-specific expert-agent prompts orchestrated across five sequential ph…

Clinical risk prediction models for worsening heart failure events and all-cause mortality in adults with mild-to-moderate chronic kidney disease.

INTRODUCTION: To develop and internally validate electronic health record (EHR)-based machine-learning models to predict worsening heart failure (WHF) events across care settings and all-cause mortality among adults with mild-to-moderate chronic kidney disease (CKD). METHODS: We studied adults with mild-to-moderate CKD [estimated glomerular filtration rate (eGFR) 30-59 ml/min/1.73 m² or eGFR ≥60 with albuminuria] receiving care in a large health system from 2012 to 2021; outcomes were ascertained through 31 December 2022. Primary outcomes were (i) WHF events-outpatient encounters, emergency d

Multimodal artificial intelligence in prostate cancer: integrating multiparametric MRI with clinicopathological, molecular, and functional imaging data.

Multimodal artificial intelligence (AI) is reshaping prostate cancer imaging by moving beyond MRI-only algorithms toward models that integra … g and local staging, whereas pathology-clinical fusion may support prognosis. Foundation models and large language models may facilitate transferable representation learning and conversion of unstructured reports and clinical notes into structured multimodal inputs, but hallucination, provenance, privacy, and external-validation concerns preclude autonomous use. Active …