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All Diagnostics & Imaging Drug Discovery Mental Health Hospitals & Care

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Machine learning-assisted prediction of 5-year mortality in chronic kidney disease: the KoreaN cohort study for Outcome in patients With Chronic Kidney Disease (KNOW-CKD)

Mortality prediction models for patients with non-dialysis chronic kidney disease (CKD) remain limited despite their clinical importance. While machine learning (ML) offers the potential to improve prediction accuracy, its "black-box" nature has hindered clinical adoption. This study aimed to develop and validate an interpretable ML model for predicting 5-year all-cause mortality in patients with non-dialysis CKD and to deploy it as a user-friendly web-based risk stratification tool. We analyzed 1,858 patients (…

Kidney Research and Clinical Practice · Health

Machine learning-assisted prediction of 5-year mortality in chronic kidney disease: the KoreaN cohort study for Outcome in patients With Chronic Kidney Disease (KNOW-CKD)

Both readings

An unsupervised machine learning analysis of biopsychosocial characteristics and treatment outcome of alcohol use disorder

Background: Alcohol Use Disorder is a heterogeneous condition where standard severity measures often fail to predict individual treatment responses. Precision medicine requires identifying distinct biopsychosocial profiles to guide targeted interventions.Objectives: To identify clinically meaningful Alcohol Use Disorder profiles using k-means clustering based on eight baseline biopsychosocial variables and validate their prognostic utility by comparing treatment outcomes.Methods: A retrospective observational st…

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Evidence-backed problem

Power, privilege and moral responsibility: learning from I.M. Young's Social Connection Model in the context of AI-driven healthcare

Artificial intelligence (AI) in healthcare is assumed to introduce risks that are not easily addressed by dominant philosophical models for thinking about responsibility. When an AI tool makes an error that results in patient harm, the question of who is responsible is rarely straightforward. Dominant models of responsibility work when harm can be traced to a single actor, but they fail in socio-technical systems where decisions and actions are distributed across multiple human and technological agents. Iris Mar…

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Both readings

'I've talked to ChatGPT about my issues last night.': Examining Mental Health Conversations with Large Language Models through Reddit Analysis

We investigate the role of large language models (LLMs) in supporting mental health by analyzing Reddit posts and comments about mental health conversations with ChatGPT. Our findings reveal that users value ChatGPT as a safe, non-judgmental space, often favoring it over human support due to its accessibility, availability, and knowledgeable responses. ChatGPT provides a range of support, including actionable advice, emotional support, and validation, while helping users better understand their mental states. Ad…

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Evidence-backed gain

The Role of Artificial Intelligence Combined With Digital Cholangioscopy for Indeterminant and Malignant Biliary Strictures: A Systematic Review and Meta-analysis

Background Current endoscopic retrograde cholangiopancreatography (ERCP) and cholangioscopic-based diagnostic sampling for indeterminant biliary strictures remain suboptimal. Artificial intelligence (AI)-based algorithms by means of computer vision in machine learning have been applied to cholangioscopy in an effort to improve diagnostic yield. The aim of this study was to perform a systematic review and meta-analysis to evaluate the diagnostic performance of AI-based diagnostic performance of AI-associated chol…

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Evidence-backed gain

Design and optimization of deep learning model based on multimodal data fusion for dynamic mental health assessment

The dynamic assessment of mental health has emerged as a hotspot for study and application due to the rise in social pressure. However, onventional methods rely mostly on static scales or single-modal data, failing to fully capture multifaceted emotional and behavioral features. This study suggests a deep learning model based on multi-modal data fusion to address this problem. By combining information from multiple sources, including text and visuals, the model effectively identifies and dynamically monitors men…

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Evidence-backed gain

The use of machine learning models for subdural hematoma detection: a single-arm meta-analysis

Manual evaluation of non-contrast CT scans (NCTS) for detecting subdural hematoma (SDH) is time consuming, potentially inaccurate, and subjective to the expert analyzing them. In recent years, two deep learning (DL) algorithms have been popularly studied in this respect, namely convolutional neural networks (CNN) and U-Net architectures, the latter being a specialized type of CNN. We performed the first meta-analysis comparing various DL models for SDH detection. MEDLINE, Cochrane, Scopus, and Embase databases w…

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Machine learning-enabled prediction of ART pregnancy outcomes: a systematic review and meta-analysis

Objective To systematically evaluate the diagnostic accuracy and methodological quality of machine learning (ML) prediction models for pregnancy outcomes after assisted reproductive technology (ART). Methods PubMed, Embase, the Cochrane Library, IEEE Xplore, MEDLINE, ClinicalTrials.gov, CNKI, Wanfang, and VIP were searched from inception to July 2026. Eligible studies developed or validated ML models to predict clinical pregnancy or live birth after ART. For studies reporting complete 2 × 2 contingency data, poo…

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Comparative evaluation of large language models and clinicians in real-world glaucoma clinical reasoning

Purpose Clinical decision-making in glaucoma is complex and requires integration of heterogeneous information, including patient history, examination findings, and risk stratification. While artificial intelligence (AI) has shown strong performance in image-based ophthalmic tasks, its capability in specialty-specific clinical reasoning remains insufficiently explored. Methods Performance was evaluated by glaucoma specialists using a predefined rubric across three clinically oriented domains: medical accuracy (40…

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Clinical Research on Microecological Landscape for Infection Risk Stratification in Newly Diagnosed Patients with Hematological Conditions

Introduction Infection is a common and potentially fatal complication during the treatment of hematological diseases, particularly in the context of chemotherapy-induced immunosuppression. The nonselective use of antibiotic prophylaxis in patients with neutropenia in China has persistently accelerated antimicrobial resistance. Early identification of patients at high risk for infection before clinical symptom onset could enable targeted preventive strategies; however, reliable and biologically informed screening…

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Clinical Research on Microecological Landscape for Infection Risk Stratification in Newly Diagnosed Patients with Hematological Conditions

The ethical challenges in the integration of artificial intelligence and large language models in medical education: A scoping review

With the rapid development of artificial intelligence (AI), large language models (LLMs), such as ChatGPT have shown potential in medical education, offering personalized learning experiences. However, this integration raises ethical concerns, including privacy, autonomy, and transparency. This study employed a scoping review methodology, systematically searching relevant literature published between January 2010 and August 31, 2024, across three major databases: PubMed, Embase, and Web of Science. Through rigor…

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Artificial Intelligence in Nutrition and Dietetics: A Comprehensive Review of Current Research

Background/Objectives: Artificial intelligence (AI) has emerged as a transformative force in healthcare, with nutrition and dietetics becoming key areas of application. AI technologies are being employed to enhance dietary assessment, personalize nutrition plans, manage chronic diseases, deliver virtual coaching, and support public health nutrition. This review aims to critically synthesize the current literature on AI applications in nutrition, identify research gaps, and outline directions for future developme…

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Determining groundwater quality and its associated human health risk using hydrochemical signatures and some machine learning techniques

Groundwater is a major source of domestic water in coastal Ghana, but its quality is increasingly threatened by salinisation, nutrient enrichment, geogenic mineralisation, and localised anthropogenic contamination. In addition to hydrochemical indices and multivariate statistics that have been used over the years to assess coastal water quality, this study has incorporated nonlinear machine learning and probabilistic risk assessment to enhance source discrimination and uncertainty-based health risk characterisat…

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Factors associated with attitudes towards artificial intelligence among medical students: roles of digital literacy, emotional intelligence, and AI-related perceptions

Artificial intelligence is increasingly used in health care and medical education. This study aimed to identify factors associated with medical students' attitudes towards artificial intelligence, with particular attention to digital literacy, emotional intelligence and artificial intelligence-related perceptions. This cross-sectional study was conducted between November 2025 and January 2026 among 358 medical students. Data were collected using an online questionnaire including sociodemographic items, the Trait…

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Factors associated with attitudes towards artificial intelligence among medical students: roles of digital literacy, emotional intelligence, and AI-related perceptions

TrialTriage, a Semiautonomous Prescreening Workflow for Resolving Ambiguity in Phase I Oncology Trial Eligibility: Development and Proof-of-Concept Study Using Synthetic Cases

Enrollment in phase I oncology trials remains low largely because potentially eligible patients are not identified and evaluated quickly enough. Current clinical trial matching systems can identify candidate patients from the electronic health record, but cases with missing or uncertain eligibility data are often routed for offline manual review. This delay impedes clarification and prolongs the final eligibility determination. This study evaluated TrialTriage, a semiautonomous system built on the n8n platform a…

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TrialTriage, a Semiautonomous Prescreening Workflow for Resolving Ambiguity in Phase I Oncology Trial Eligibility: Development and Proof-of-Concept Study Using Synthetic Cases