Healthcare

Extracting insights from Electronic Health Record (EHR) databases often requires SQL expertise, creating a barrier for clinical decision-making and research. A promising approach is to use Large Language Models (LLMs) to translate natural language questions into SQL through Retrieval-Augmented Generation (RAG), where relevant question-SQL examples are retrieved to generate new queries via few-shot learning. However, adapting this method to the medical domain is non-trivial, as effective retrieva

With the rapid advancement of large language model (LLM) technologies, AI agents have rapidly emerged in healthcare. This review traces the historical evolution and core characteristics of AI agents, and systematically examines their applications in assisted diagnosis, clinical decision support, medical report generation, patient-facing chatbots, healthcare system management, and medical education. We further analyze existing evaluation frameworks for AI agents in healthcare, focusing on key dim

Natural products (NPs) have long served as a cornerstone of drug discovery, yielding landmark therapeutics such as paclitaxel and artemisinin and providing sustained access to biologically relevant chemical space. Despite this legacy, NP-based discovery has gradually declined with the rise of synthetic chemistry and high-throughput screening, even as many contemporary "synthetic" drugs remain structurally inspired by natural scaffolds. Classical NP workflows-centered on phenotypic screening and

The field of food and medicine homology (FMH) is transitioning from traditional empirical knowledge to contemporary scientific methodologies.Commencing in 2026, this evolution will be anchored by a core tenet: fostering trust through scientific rigor, enhancing mechanistic comprehension via systematic analysis, refining health interventions with the concept of precision medicine, and modernizing the entire industrial chain using cutting-edge technologies.The progression within this domain will b

Introduction Vulvovaginal candidiasis (VVC) is one of the most common fungal infections requiring more effective and patient-friendly therapies. This study introduces repurposed Ketoprofen (KPN) Quatsomes (QS) as a novel nano-platform for localized antifungal treatment. Methods KPN-QS were prepared using quaternary ammonium surfactants and cholesterol via probe sonication and optimized through a 3 1 × 2 2 mixed factorial design using Design-Expert ® software. The effects of quaternary ammonium s

Abstract Clinical prediction models are often created using large routinely collected datasets. It is essential that prediction models are developed with appropriate data and methods and transparently reported to ensure that decisions are based on reliable predictions. Kaggle is a popular competition and data repository website where users learn and apply analysis skills on a range of datasets. We identified two large, publicly available Kaggle datasets, on stroke and diabetes, that lack clear d

Biological aging reflects complex cellular and biochemical processes that can be measured across multiple omic layers. Using routine clinical laboratory data from ~31,000 participants in the Mass General Brigham Biobank, we developed EMRAge, a biomarker of mortality risk that can be broadly recapitulated across electronic medical records. Here we show that EMRAge can be modeled using elastic net regression with DNA methylation and multi-omics to generate DNAmEMRAge and OMICmAge, respectively. Bo

BACKGROUND: The integration of artificial intelligence in healthcare has transformed clinical practice and research methodologies. However, concerns regarding algorithmic accountability, interpretability, and safety have necessitated human oversight in AI systems. Human in the loop artificial intelligence represents a collaborative paradigm where human expertise and machine intelligence converge to enhance decision making while maintaining ethical standards and clinical safety. AIM: This review

Agentic artificial intelligence (AI) represents a pivotal shift in clinical decision support, moving beyond static tools by reasoning, adapting, and acting alongside clinicians. Psychiatry, grounded in subjective experience, trust, and longitudinal care, offers both an opportunity and a high-stakes testbed. Agentic systems may enhance documentation, personalize care, support continuous monitoring, and extend access, while raising risks around bias, explainability, privacy, and therapeutic allian

Millions of patients are regularly using large language model (LLM) chatbots for medical advice, raising patient safety concerns. This physician-led red-teaming study compares the safety of four publicly available chatbots-Claude by Anthropic, Gemini by Google, GPT-4o by OpenAI, and Llama-3.0/3.1-70B by Meta-on a new dataset, HealthAdvice, using an evaluation framework that enables quantitative and qualitative analysis. In total, 888 chatbot responses are evaluated for 222 patient-posed advice-s

The doctor-patient relationship is a central factor in healthcare delivery. Artificial Intelligence (AI) represents an emerging technological frontier whose implications remain to be fully clarified. Evidence-based studies provide reliable analyses of effects and offer a deeper understanding of both limits and benefits. This narrative review aimed to explore the role of AI in modern clinical practice, with particular reference to its effects on the doctor-patient relationship. Scopus and Web of

Artificial Intelligence (AI) deployment in healthcare is accelerating, yet governance frameworks remain fragmented and often assume extensive resources. Through a systematic review of 35 frameworks for AI implementation in healthcare (published 2019-2024), we identified seven critical domains of healthcare AI governance. While existing frameworks provide valuable guidance, the resource requirements create barriers for smaller healthcare organizations. To address this gap, we organized key findin

The oral cavity contains a complex and dynamic microbial ecosystem that plays a central role in maintaining both local and systemic homeostasis. Emerging evidence indicates that disturbances in oral microbial communities-including genetic and functional diversity within species-are associated not only with oral diseases but may also contribute to the development and progression of systemic diseases. This narrative review summarises the current state of knowledge on bidirectional interactions bet

Artificial intelligence (AI) is rapidly shifting from experimental pilots to mainstream clinical infrastructure, redefining how evidence, accountability, and ethics intersect in healthcare. This narrative review integrates insights from peer-reviewed studies and policy frameworks to examine seven cross-cutting aspects: bias and fairness, explainability, safety and quality, privacy and data protection, accountability and liability, human oversight, and procurement and deployment. Findings reveal

Hematopoietic stem cell transplantation (HSCT) remains a cornerstone treatment for many hematological malignancies, but its clinical success is still challenged by graft-vs.-host disease (GvHD), infectious complications, and the profound microbial disruptions caused by conditioning, antibiotics, and hospitalization. Over the past few years, a growing body of work has highlighted how tightly post-transplant immunity is linked to the state of the gut microbiota. In particular, short-chain fatty ac

Artificial intelligence applied to brain magnetic resonance imaging (MRI) holds potential to advance diagnosis, prognosis and treatment planning for neurological diseases. The field has been constrained, thus far, by limited training data and task-specific models that do not generalize well across patient populations and medical tasks. By leveraging self-supervised learning, pretraining and targeted adaptation, foundation models present a promising paradigm to overcome these limitations. Here we

Background/Objectives: Periodontitis is a chronic inflammatory disease with marked inter-individual heterogeneity and well-established links to cardiometabolic and other systemic conditions. Conventional clinical diagnostics remain indispensable. However, they provide limited real-time insight into molecular activity and host-response biology. This review aimed to synthesize recent advances in point-of-care diagnostics and emerging molecular biomarkers relevant to periodontal disease and its sys

Global healthcare providers are exploring the use of large language models (LLMs) to provide medical advice to the public. LLMs now achieve nearly perfect scores on medical licensing exams, but this does not necessarily translate to accurate performance in real-world settings. We tested whether LLMs can assist members of the public in identifying underlying conditions and choosing a course of action (disposition) in ten medical scenarios in a controlled study with 1,298 participants. Participant

BACKGROUND: Radiology reports are typically written in language that is difficult for patients to understand. Large language models (LLMs) excel at simplifying text. We aimed to evaluate the ability of LLMs to improve the understanding of radiology reports. METHODS: In this systematic review and meta-analysis, we searched CENTRAL, MEDLINE, and Embase from inception to Nov 11, 2025, without restrictions on language. Full-text articles and preprints were considered for inclusion. Eligible studies

The digital twin (DT) concept, originating from engineering disciplines, has emerged as a transformative technology in healthcare, particularly in oncology. A digital twin creates a dynamic, virtual replica of a patient's physiological and pathological state, integrating multi-dimensional data to enable personalised cancer care. Despite growing interest, comprehensive reviews examining the breadth of DT applications in oncology remain limited. This narrative review aims to synthesise current evi

Introduction: Vaginal candidiasis remains a recurrent fungal infection affecting millions of women worldwide, necessitating innovative local delivery systems to overcome poor drug solubility and mucosal barriers. This study introduces Dual-Functional Hybrid Cationic PEGylated Proniosomes (DHCPP) as a smart nano-platform designed to boost the vaginal delivery of Fenticonazole Nitrate (FTN). Methods: DHCPP systems were fabricated via the coacervation phase separation method and optimized using a f

BACKGROUND: Early detection of cardiovascular disease is a global public health priority. Artificial intelligence (AI)-enabled stethoscopes offer robust performance characteristics in point-of-care detection of heart failure, atrial fibrillation, and valvular heart disease (VHD). We conducted a pragmatic, cluster-randomised controlled implementation trial to determine the real-world effect and implementation challenges of AI-stethoscopes. METHODS: UK primary care practices were cluster randomise

Objectives: This study aims to evaluate whether contemporary artificial intelligence (AI), including convolutional neural networks (CNNs) for medical imaging and large language models (LLMs) for language processing, could replace physicians in the near future and to identify the principal clinical, technical, and regulatory barriers. Methods: A narrative review is conducted on the scientific literature addressing AI performance and reproducibility in medical imaging, LLM competence in medical kn

Artificial intelligence (AI) is entering routine radiology practice, but most studies evaluate algorithms in isolation rather than their interaction with radiologists in clinical workflows. This narrative review summarizes current knowledge on human-AI interaction in radiology and highlights practical risks and opportunities for clinical teams. First, simple conceptual models of human-AI collaboration are described, such as diagnostic complementarity, which explain when radiologists and AI can a

Background: Contemporary stroke rehabilitation protocols traditionally emphasise session frequency and treatment duration over intervention intensity-yet emerging evidence suggests we may be preparing patients for therapeutic marathons when their brains demand neuroplastic sprints. Across neuroscientific, behavioural, and clinical domains, convergent data indicate that repetition density, metabolic load, engagement, and temporal compression-not cumulative minutes-constitute the biologically mean

Digital media, including television, the internet, social media, video games, and interactive assistants, form the digital ecosystem. When this digital ecosystem is designed with children's unique developmental needs in mind, it can support learning and well-being. In contrast, digital ecosystems that prioritize engagement and commercialization often encourage prolonged use, which in turn can displace healthy behaviors (eg, movement behaviors, sleep), and contribute to negative outcomes. This po

ABSTRACT As an emerging branch of clinical medicine, microbiota medicine has attracted worldwide attention from clinicians, medical educators, patient communities, and industry. However, this developing field still lacks consensus on its fundamental principles as well as guidelines for clinical and educational practice. An expert panel was convened by the journal Microbiota Medicine Research at the 2025 CHINAGUT Conference to develop the principles and practice guidelines of microbiota medicine

Introduction: The European Health Data Space (EHDS), the European Union's new regulatory framework for health data use and reuse, will have important implications for citizens across the Union. While the regulation aims to empower citizens in the primary use of their health data-such as by giving them access to their electronic health records-their role in the secondary use of health data remains less clearly defined. Methods: To explore this, we interviewed health data experts across 23 Europea

Sepsis is a major global health crisis where early recognition and effective management remain significant challenges for healthcare systems. As part of the Lausanne University Hospital sepsis quality of care program, we developed and validated an Artificial Intelligence (AI)-powered Sepsis Learning Health System (SLHS) to enhance sepsis care. The SLHS combines a standardized clinical pathway with HERACLES, an AI algorithm that retrospectively classifies patient data into confirmed, possible, or

In recent years, much of the research on clustering algorithms has primarily focused on enhancing their accuracy and efficiency, frequently at the expense of interpretability. However, as these methods are increasingly being applied in high-stakes domains such as healthcare, finance, and autonomous systems, the need of transparent and interpretable clustering outcomes has become a critical concern. This is not only necessary for gaining user trust but also for satisfying the growing ethical and

Background: Metabolic syndrome, a clinical condition defined by central obesity, impaired glucose regulation, elevated blood pressure, hypertriglyceridemia, and low high-density lipoprotein cholesterol across the lifespan, is now a major public health issue typically managed with lifestyle, behavioral, and dietary recommendations. However, “one-size-fits-all” recommendations often yield modest, heterogeneous responses and poor long-term adherence, creating a clinical need for more targeted and i

Large language models (LLMs) are emerging as powerful tools in healthcare, with a growing role in global health, particularly in low- and middle-income countries (LMICs). This Perspective examines the current progress, challenges and prospects of LLMs in addressing health system disparities and supporting the achievement of the Sustainable Development Goals (SDGs). While high-income countries dominate the development and deployment of LLMs, LMICs face substantial barriers. These include limited

Introduction: Artificial intelligence (AI) holds profound potential to reshape public health through enhanced disease prediction, diagnosis, and health management. However, this technological advancement is accompanied by significant ethical, social, and governance challenges. This systematic review aims to comprehensively examine the opportunities and challenges of AI in public health, focusing on its applications, associated dilemmas, and governance pathways. Methods: This review was conducted

By leveraging everyday technologies such as mobile apps, wearables, and AI-enabled tools, digital health interventions (DHIs) offer new pathways to integrate self-management and intervention programs into the fabric of daily life, while bridging gaps in care through continuous, context-aware support. Yet many tools underperform clinically because digital engagement ("screen time") is conflated with impact, while behavioral science is retrofitted, if applied at all. We propose the ENGAGE Framewor

Introduction Despite the potential of artificial intelligence (AI) to transform healthcare delivery and reduce costs, adoption remains uneven across populations. Understanding the demographic, behavioral, and cognitive factors influencing public willingness to use AI-powered health tools is critical for equitable implementation. This study examined determinants of AI adoption in healthcare among adults in the United States (U.S.). Methods A cross-sectional survey was conducted between March and

BACKGROUND: Genioplasty and chin-augmentation are well-established procedures aimed at enhancing lower facial aesthetics. Traditionally, aesthetic outcomes have been assessed subjectively through expert opinions and patient-reported measures. The integration of artificial intelligence (AI) offers an objective approach to evaluating surgical results. This study utilizes the ICA Aesthetic Navigation AI Research Metrics Model (ICAAN® ARMM) to analyze postoperative changes in facial attractiveness,

While nutraceuticals hold great promise for improving health, their efficacy is often limited by the poor stability and low bioavailability of many bioactive compounds. Nanoencapsulation has emerged as a transformative solution to these challenges, involving nanoscale carriers that protect sensitive nutrients from degradation and enhance their absorption. This review provides a comprehensive overview of nanoencapsulation strategies in the food and nutrition domain. We outline the historical deve

INTRODUCTION: This study evaluated the effectiveness of Moderna's updated mRNA-1273 vaccine targeting the KP.2 variant, compared to people who did not receive any 2024-2025 COVID-19 vaccine, in preventing COVID-19-associated hospitalizations and medically-attended COVID-19 among adults aged ≥ 18 years in the United States during the 2024-2025 season. METHODS: Data were extracted from linked administrative healthcare claims and electronic health records (EHR) for vaccinations from 23 August 2024

To compare the diagnostic performance of a subscription-based medical large language model (LLM) certified as a medical device (Prof. Valmed), a subscription-based general-purpose LLM (ChatGPT-5 Thinking), and a freely accessible medical LLM (OpenEvidence). Sixty vignettes covering rare rheumatic diseases and differential diagnoses were entered using a standardized prompt to generate five top diagnoses and respective diagnostic probabilities. Blinded rheumatologists categorized suggested diagnos

Background: Diabetic kidney disease (DKD) is a major microvascular complication of diabetes, often progressing silently and leading to end-stage kidney disease (ESKD) and cardiovascular morbidity. Early identification and risk-adapted intervention are crucial to improving long-term outcomes, yet existing clinical workflows are limited by delayed diagnosis and underutilization of available therapies. Methods: We propose and evaluate a multimodal, risk-driven framework for the early recognition an