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Machine learning prognostication in nasopharyngeal carcinoma: a european multicentre analysis of survival and risk of second malignancy

Nasopharyngeal carcinoma (NPC) is rare in Europe, and emerging data suggest poorer outcomes in Caucasian patients compared with Asian populations, highlighting the need for region-specific prognostic tools. Inflammation-based biomarkers and artificial intelligence show promise for risk stratification and prediction of survival and second primary cancers (SPCs). We conducted a retrospective multicentre study including 405 NPC patients from six European institutions. Demographic, clinicopathological, and haematolo…

European Archives of Oto-Rhino-Laryngology

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Predictive value of the uric acid to high-density cholesterol ratio (UHR) combined with intact parathyroid hormone for protein-energy wasting after incident hemodialysis: a multicenter study

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Beyond BMI: Deep Learning Segmentation-Driven CT Reveals Body Composition Changes after Metabolic and Bariatric Surgery

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Medical student reliance on artificial intelligence in nephrology education

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Randomized, Double-Blind, Placebo-Controlled First-in-Human Trial of a First-in-Class AI-Designed Monoclonal Antibody (GB-0669) Against the Conserved SARS-CoV-2 Spike S2 Stem Helix

Background Antibodies against the SARS-CoV-2 spike receptor-binding domain provided effective COVID-19 treatment until resistant variants emerged. GB-0669 is a half-life-extended monoclonal antibody optimized using artificial intelligence. It targets the conserved spike S2 stem helix, a region subject to limited selective pressure from antibody responses induced by natural infection or vaccination. Methods Pre-clinical safety studies were conducted in cynomolgus monkeys. In the first-in-human trial, healthy adul…

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Comprehensive analysis of neutrophil extracellular traps-associated inflammatory genes for patients with idiopathic pulmonary fibrosis

Neutrophil extracellular traps (NETs) facilitate inflammation and epithelial-mesenchymal transition (EMT), promoting the progression of pulmonary fibrosis. Various machine learning methods were used to screen for prognostic genes. Based on prognostic genes, a risk model was constructed to assess their ability for prognosis prediction of idiopathic pulmonary fibrosis (IPF). Mendelian Randomization (MR) analysis evaluated causal associations between IPF and prognostic genes, while GSE122960 examined cell-type-spec…

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Urinary Metabolic Age from High-Resolution NMR Reveals Longitudinal Aging Patterns

Biological age captures inter-individual heterogeneity in aging process arising from genetic and environmental influences. Metabolites, as the end-products of metabolism, integrate these factors and are therefore well suited for biological age estimation. Urinary metabolomics, in particular, provides a non-invasive and information-rich matrix for assessing systemic metabolic states. We applied different machine learning techniques to develop a biological age score from high-resolution 1H nuclear magnetic resonan…

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Beyond bias: using AI to reduce diagnostic noise and manage novelty in clinical reasoning

Objectives This study examines AI's capacity to mitigate noise-related diagnostic errors, evaluates its impact on accuracy, and explores the interplay between AI-driven efficiency and human clinical reasoning, particularly in rare or complex cases. Background: Diagnostic errors in clinical reasoning are significantly influenced by noise - random unwanted variability in expert judgments - distinct from cognitive biases. Despite debiasing efforts, noise persists, contributing to adverse events. Artificial intellig…

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Google DeepMind launches AI tool to help identify genetic drivers of disease

Researchers at Google DeepMind have unveiled their latest artificial intelligence tool and claimed it will help scientists identify the genetic drivers of disease and ultimately pave the way for new treatments. AlphaGenome predicts how mutations interfere with the way genes are controlled, changing when they are switched on, in which cells of the body, and whether their biological volume controls are set to high or low. Most common diseases that run in families, including heart disease and autoimmune disorders,…

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Random Forest

For the task of analyzing survival data to derive risk factors associated with mortality, physicians, researchers, and biostatisticians have typically relied on certain types of regression techniques, most notably the Cox model. With the advent of more widely distributed computing power, methods which require more complex mathematics have become increasingly common. Particularly in this era of "big data" and machine learning, survival analysis has become methodologically broader. This paper aims to explore one t…

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AI-driven multimodal retinal imaging for early detection and risk stratification of vascular and neurodegenerative diseases

Systemic vascular and neurodegenerative disorders are important causes of disability and death worldwide, mainly because of the late stage of diagnosis and the high cost of current screening tools. Artificial intelligence (AI) and multimodal retinal imaging offer a non-invasive and viable approach for early risk stratification and longitudinal monitoring. This review highlights how changes in the retinal vasculature and nerve layers are markers of underlying pathophysiologies related to cardiovascular, metabolic…

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Evolving surgical teams in the age of artificial intelligence and robotics

Surgery is a critical function of the healthcare system, key to addressing a substantial portion of the global disease burden. The integration of advanced artificial intelligence (AI) and robotics ecosystems into the operating room (OR) promises to radically transform surgery, with profound implications. This article analyzes the current state of surgical AI and robotic systems; presents a vision for their future, highlighting technological and research challenges and their associated impact on surgical teams; a…

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Opportunities and Challenges in Using National EHR Networks for AI in Learning Health Systems

Background: National electronic health record (EHR) networks can support learning health systems (LHSs) by enabling large-scale data aggregation, monitoring, and benchmarking, but their capacity to produce trustworthy and locally deployable machine learning and artificial intelligence (ML/AI) models remains uncertain. We characterized major US national EHR networks and examined barriers to ML/AI development and deployment across the LHS cycle. Methods: We conducted an environmental scan combining PubMed searches…

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Exploring Students’ Perceptions and Usage of Artificial Intelligence in Supporting Mental Health: A Preliminary Study in Higher Education in Qatar

Background: Artificial intelligence (AI) is widely used in mental health care for screening, monitoring, and intervention. Notably, most studies of AI in mental health have been performed in Western contexts, with limited evidence from the Arab Gulf region, where cultural factors such as stigma, privacy, and help-seeking norms may influence acceptance. Objective: Investigating university students’ perceptions of AI in mental health support, including awareness, trust, readiness, and preferences in a Gulf context…

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Barriers and Facilitators to the Use of Large Language Model-Based Conversational Agents in Mental Healthcare: A Systematic Review

(1) Background/Objectives: Over one billion individuals globally live with mental health conditions, yet the treatment gap exceeds 75% in low- and middle-income countries. Large language model (LLM)-based conversational agents have emerged as a potentially scalable solution, though the evidence base remains nascent and largely pre-clinical. This review synthesises barriers and facilitators to their implementation in mental healthcare using the Consolidated Framework for Implementation Research (CFIR). (2) Method…