Healthcare

Artificial intelligence is increasingly being used in medical practice to complete tasks that were previously completed by the physician, such as visit documentation, treatment plans and discharge summaries. As artificial intelligence becomes a routine part of medical care, physicians increasingly trust and rely on its clinical recommendations. However, there is concern that some physicians, especially those younger and less experienced, will become over-reliant on artificial intelligence. Over-

Objectives: This study aims to elucidate the pivotal role of Artificial Intelligence (AI) in driving the transformation of precision medicine, comprehensively analyzing how it reshapes healthcare systems from traditional diagnosis and treatment paradigms into personalized health management ecosystems. Methods: A comprehensive narrative review was conducted to systematically synthesize and critically evaluate the innovative applications, paradigm shifts, and future prospects of AI across the enti

Precision healthcare is increasingly oriented toward the development of therapeutic strategies that are as individualized as the patients receiving them. Central to this paradigm shift is artificial intelligence (AI)-enabled multi-modal data integration, which consolidates heterogeneous data streams-including genomic, transcriptomic, proteomic, imaging, environmental, and electronic health record (EHR) data into a unified analytical framework. This integrative approach enhances early disease det

Abstract Mental health disorders are becoming a major global health concern and pose a significant burden on global healthcare systems. Nearly one billion people suffer from mental disorders, accounting for 13% of the global disease burden and $1 trillion in annual productivity loss. Depression is the leading cause of disability and suicide is the second leading cause of death among young individuals. Economic uncertainty, social isolation, climate change, shifting societal norms, political conf

Background Grit, defined as perseverance and passion for long-term goals, is a vital psychological trait that contributes to academic success and overall wellbeing. At the same time, regular PA supports physical and mental health, yet its engagement often declines among university students. Although grit has been linked to health-promoting behaviors, the mechanisms underlying this association remain unclear. This study investigated the mediating role of self-efficacy from a variable-centered per

DNA methylation is a central epigenetic mechanism that mediates the interaction between nutritional exposures and gene regulation. Emerging evidence demonstrates that diet, bioactive compounds, genetic background, and lifestyle factors collectively shape the human methylome, influencing metabolic function, disease susceptibility, and biological aging. This review synthesizes current knowledge on the molecular and biochemical mechanisms of DNA methylation, the role of nutrients and dietary patter

Clinical reasoning is foundational to medical practice, requiring clinicians to synthesise complex information, recognise patterns, and apply causal reasoning to reach accurate diagnoses and guide patient management. However, human cognition is inherently limited by factors such as limitations in working memory capacity, constraints in cognitive load, a general reliance on heuristics; with an inherent vulnerability to biases including anchoring, availability bias, and premature closure. Cognitiv

Objective To investigate the correlation of β-C-terminal telopeptide of type I collagen (β-CTX), procollagen type I N-terminal propeptide (PINP), alkaline phosphatase (ALP) with bone mineral density (BMD) in patients with osteoporosis and evaluate their predictive value for secondary fracture risk. Methods A total of 180 osteoporosis patients and 80 healthy controls were enrolled. The osteoporosis group was stratified into fracture and non-fracture cohorts. Correlation of β-CTX, PINP, ALP with B

Accurate Air Quality Index (AQI) classification is essential for environmental surveillance and public health decision-making. Using a publicly available daily U.S. county-level dataset with six AQI categories (Good, Moderate, Unhealthy for Sensitive Groups, Unhealthy, Very Unhealthy, Hazardous), we conducted a comprehensive benchmarking study. Data preprocessing included missing-value imputation and class balancing via Synthetic Minority Over-sampling Technique (SMOTE). We trained and evaluated

Wearable sensors are transforming real-time, non-invasive health monitoring. Despite considerable advances in electrochemical and optical sensing modalities, challenges remain in achieving reliable, sensitive, and cost-effective detection of sweat metabolites due to the variable chemical composition of sweat and difficulties in device integration. Electrochemiluminescence (ECL) offers an attractive alternative for wearable sensing due to its high sensitivity and ease of integration without compl

Bacteriophage therapy, which employs bacterial viruses to selectively eliminate pathogenic bacteria, has re-emerged as a promising strategy in the face of increasing antimicrobial resistance. However, its widespread clinical implementation is constrained by concerns regarding safety, standardisation, and predictable efficacy. In this review, we examine the key role of genomics in transforming phage therapy from an empirical practice into a standardised and personalised modality of contemporary m

Differential privacy (DP) is a prominent technique for protecting sensitive patient data in medical deep learning (DL), yet deploying it without compromising clinical utility or equity remains challenging. This scoping review synthesizes applications of DP in medical DL across centralized and federated settings. A structured search identified 74 eligible studies published through March 2025. Across modalities and tasks, DP, especially via DP-SGD, can maintain clinically acceptable performance un

This perspective analyzes the substantial advantages of Artificial Intelligence (AI) and machine learning (ML) in improving the efficacy and precision of biosensors, facilitating accurate detection of diverse physiological signals. Moreover, it emphasizes contemporary developments in biosensor technology and their uses in medical diagnosis, stressing their ability for early disease detection and continuous monitoring. The study also addresses major barriers to more widespread use, such as the la

Background: Automated brain tumor segmentation on multi-parametric magnetic resonance imaging (mpMRI) is crucial in assessing patient outcomes and remains a challenge across Sub-Saharan Africa (SSA). Since 2012, the Brain Tumor Segmentation (BraTS) Challenge has evaluated state-of-the-art artificial intelligence (AI) methods to detect, characterize, and classify tumors. However, it is unclear if these methods can generalize, and hence be widely implemented, in SSA populations. To address this, t

With the advent of Large Language Models (LLMs), medical artificial intelligence (AI) has experienced substantial technological progress and paradigm shifts, highlighting the potential of LLMs to streamline healthcare delivery and improve patient outcomes. Considering this rapid technical progress, in this survey, we trace the recent advances of Medical Large Language Models (Med-LLMs), including the background, key findings, and mainstream techniques, especially for the evolution from general-p

BACKGROUND: With the continuous advancement of cancer treatment technology, the proportion of cancer survivors is gradually increasing, but they also face many psychological challenges. These challenges can seriously affect their quality of life. Telemedicine, as an innovative medical service model, can be combined with psychosocial intervention to provide cancer survivors with convenient, economical and accessible services to assist them in more effectively managing the difficulties posed by ca

Healthcare systems worldwide face growing challenges, including rising costs, workforce shortages, and disparities in access and quality, particularly in low- and middle-income countries. Artificial intelligence (AI) has emerged as a transformative tool capable of addressing these issues by enhancing diagnostics, treatment planning, patient monitoring, and healthcare efficiency. AI's role in modern medicine spans disease detection, personalized care, drug discovery, predictive analytics, telemed

Aims/Background: The growing integration of artificial intelligence (AI) into clinical medicine has opened new possibilities for enhancing diagnostic accuracy, therapeutic decision-making, and biomedical innovation across several domains. This review is aimed to evaluate the clinical applications of AI across five key domains of medicine: diagnostic imaging, clinical decision support systems (CDSS), surgery, pathology, and drug discovery, highlighting achievements, limitations, and future direct

Background: Theintegration of artificial intelligence (AI) into clinical decision support systems (CDSSs) has significantly enhanced diagnostic precision, risk stratification, and treatment planning. AI models remain a barrier to clinical adoption, emphasizing the critical role of explainable AI (XAI). Methods: This systematic meta-analysis synthesizes findings from 62 peer-reviewed studies published between 2018 and 2025, examining the use of XAI methods within CDSSs across various clinical dom

Abstract The integration of Artificial Intelligence (AI) in healthcare is reshaping clinical practice, offering both opportunities for enhanced decision-making and risks of skill degradation among medical professionals. This growing impact calls for a comprehensive evaluation of its effects on medical expertise. This study presents a mixed-method literature review, combining systematic analysis with narrative synthesis to examine AI-induced deskilling and upskilling inhibition-the erosion of med

The development of data-driven and targeted drug delivery systems is essential for advancing precision therapeutics. Despite substantial progress in nanocarrier development, conventional platforms continue to face major challenges in clinical translation due to biological complexity, off-target accumulation, and limited adaptability to dynamic physiological environments. The integration of nanoarchitectonics and artificial intelligence (AI) offers an advanced strategy for engineering delivery sy

Extracellular vesicle (EV)-based therapies have attracted considerable attention as a novel class of biologics with broad clinical potential. However, their clinical translation is impeded by the fragmented and rapidly evolving regulatory landscape, with significant disparities between the United States, European Union, and key Asian jurisdictions. In this review, we systematically analyze regional guidelines and strategic frameworks governing EV therapeutics, emphasizing critical hurdles in qua

Abstract As Artificial Intelligence (AI) becomes increasingly embedded in high-stakes domains such as healthcare, law, and public administration, automation bias (AB)—the tendency to over-rely on automated recommendations—has emerged as a critical challenge in human–AI collaboration. While previous reviews have examined AB in traditional computer-assisted decision-making, research on its implications in modern AI-driven work environments remains limited. To address this gap, this research system

Importance: Uncontrolled hypertension remains a global health concern and dysregulated aldosterone production is a central mechanism. Lorundrostat, a novel aldosterone synthase inhibitor that reduces aldosterone production, demonstrated efficacy in participants with uncontrolled hypertension, including those with treatment-resistant hypertension. Objective: To evaluate the efficacy and safety of lorundrostat for lowering blood pressure (BP) when added to a prescribed regimen of 2 to 5 antihypert

The integration of artificial intelligence (AI) with wearable bioelectronics is revolutionizing digital healthcare by enabling proactive, personalized, and data-driven medical solutions. These advanced devices, equipped with multimodal sensors and AI-powered analytics, facilitate real-time monitoring of physiological and biochemical parameters-such as cardiac activity, glucose levels, and biomarkers-allowing for early disease detection, chronic condition management, and precision therapeutics. B

This paper provides a systematic and in-depth examination of large language models (LLMs) in the healthcare domain, addressing their significant potential to transform medical practice through advanced natural language processing capabilities. Current implementations demonstrate LLMs' promising applications across clinical decision support, medical education, diagnostics, and patient care, while highlighting critical challenges in privacy, ethical deployment, and factual accuracy that require re

Automatic disease diagnosis has become increasingly valuable in clinical practice. The advent of large language models (LLMs) has catalyzed a paradigm shift in artificial intelligence, with growing evidence supporting the efficacy of LLMs in diagnostic tasks. Despite the increasing attention in this field, a holistic view is still lacking. Many critical aspects remain unclear, such as the diseases and clinical data to which LLMs have been applied, the LLM techniques employed, and the evaluation

Clinical decision-making in oncology is complex, requiring the integration of multimodal data and multidomain expertise. We developed and evaluated an autonomous clinical artificial intelligence (AI) agent leveraging GPT-4 with multimodal precision oncology tools to support personalized clinical decision-making. The system incorporates vision transformers for detecting microsatellite instability and KRAS and BRAF mutations from histopathology slides, MedSAM for radiological image segmentation an

The integration of Internet of Things (IoT) sensors and Machine Learning (ML) technologies has transformed precision agriculture by enabling data-driven, adaptive, and efficient farming practices. IoT sensors provide continuous, high-resolution monitoring of critical agricultural parameters, including soil health, crop growth, and environmental conditions. Coupled with advanced ML algorithms, this data facilitates predictive analytics and real-time decision-making, optimizing resource utilizatio

INTRODUCTION Artificial intelligence (AI) is rapidly transforming healthcare and scientific publishing. Reporting guidelines need to be updated to consider this advance.The SCARE Guideline 2025 update introduces a new AI-focused domain to promote transparency, reproducibility, and ethical integrity in surgical case reports (SCAREs) involving AI. METHODS A Delphi consensus exercise was conducted to update the SCARE guidelines. A panel of 49 surgical and scientific experts was invited to rate prop

BACKGROUND/OBJECTIVES: Artificial intelligence (AI)-enabled digital interventions are increasingly used to expand access to mental health care. This PRISMA-ScR scoping review maps how AI technologies support mental health care across five phases: pre-treatment (screening), treatment (therapeutic support), post-treatment (monitoring), clinical education, and population-level prevention. METHODS: We synthesized findings from 36 empirical studies published through January 2024 that implemented AI-d

The expanding domain of digital mental health is transitioning beyond traditional telehealth to incorporate smartphone apps, virtual reality, and generative artificial intelligence, including large language models. While industry setbacks and methodological critiques have highlighted gaps in evidence and challenges in scaling these technologies, emerging solutions rooted in co-design, rigorous evaluation, and implementation science offer promising pathways forward. This paper underscores the dua

In the rapidly evolving landscape of healthcare technology, Co-Intelligence: Living and Working with AI, by Ethan Mollick, Ph.D. (an associate professor of management and academic director of Wharton Interactive, Wharton School, University of Pennsylvania), is a timely and insightful exploration of the symbiotic relationship between humans and artificial intelligence (AI). While not specifically tailored for the medical field or anesthesiology, this book offers valuable perspectives that may res

BACKGROUND: The emergence of Artificial Intelligence (AI), particularly Chat Generative Pre-Trained Transformer (ChatGPT), a Large Language Model (LLM), in healthcare promises to reshape patient care, clinical decision-making, and medical education. This review aims to synthesise research findings to consolidate the implications of ChatGPT integration in healthcare and identify research gaps. MAIN BODY: The umbrella review was conducted following Preferred Reporting Items for Systematic Reviews

IMPORTANCE: The US healthcare system faces significant challenges, including clinician burnout, operational inefficiencies, and concerns about patient safety. Artificial intelligence (AI), particularly generative AI, has the potential to address these challenges, but its adoption, effectiveness, and barriers to implementation are not well understood. OBJECTIVE: To evaluate the current state of AI adoption in US healthcare systems, assess successes and barriers to implementation during the early

Digital health innovations are reshaping global healthcare systems by enhancing access, efficiency, and quality of care. Technologies such as artificial intelligence, telemedicine, mobile health applications, and big data analytics have been widely applied to support disease surveillance, enable remote care, and improve clinical decision making. This review critically identifies persistent implementation challenges that hinder the equitable adoption of digital health solutions, such as the digit

Large language models (LLMs) show promise in mental health care for handling human-like conversations, but their effectiveness remains uncertain. This scoping review synthesizes existing research on LLM applications in mental health care, reviews model performance and clinical effectiveness, identifies gaps in current evaluation methods following a structured evaluation framework, and provides recommendations for future development. A systematic search identified 726 unique articles, of which 16

Importance: The primary objective of any newly developed medical device using artificial intelligence (AI) is to ensure its safe and effective use in broader clinical practice. Objective: To evaluate key characteristics of AI-enabled medical devices approved by the US Food and Drug Administration (FDA) that are relevant to their clinical generalizability and are reported in the public domain. Design, Setting, and Participants: This cross-sectional study collected information on all AI-enabled me