± HEALTH Adequate self-harm surveillance is a key part of suicide prevention. Our previous research demonstrated that…± HEALTH Breast cancer remains the most commonly diagnosed malignancy among women globally, with disproportionately hi…+ HEALTH Background The prevalence of depression is higher among older adults with chronic diseases and cognitive impa…± HEALTH Purpose To compare the information quality, accuracy, and readability of patient-directed responses generated…± HEALTH Objective Comparative evaluations of machine learning (ML) and logistic regression (LR) for clinical predicti…± HEALTH Introduction Alzheimer's disease (AD) is a progressive neurodegenerative disorder for which early diagnosis-p…+ HEALTH Background Accurate identification of ganglionated bowel is essential during laparoscopic pull-through for Hi…+ CLIMATE Accurate prediction of ash fusion temperatures (AFTs) is crucial for ensuring the operational efficiency and…
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Temporal and cross-site validation of an AI system for self-harm detection

Adequate self-harm surveillance is a key part of suicide prevention. Our previous research demonstrated that an artificial intelligence (AI)-based system could effectively detect self-harm in emergency department triage notes. However, the system was developed using data from a single hospital, raising concerns about its generalisability. Here, we aim to validate the system prospectively and externally to better understand its portability across hospitals. We leveraged emergency department data from two Australi…

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Temporal and cross-site validation of an AI system for self-harm detection
Explainable machine learning for breast cancer prediction in resource-constrained settings: A multi-algorithmic framework integrating shap-based transparency with clinical decision support
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Explainable machine learning for breast cancer prediction in resource-constrained settings: A multi-algorithmic framework integrating shap-based transparency with clinical decision support

Breast cancer remains the most commonly diagnosed malignancy among women globally, with disproportionately higher mortality rates in low- and middle-income countries (LMICs) where diagnostic delays and limited specialist pathology capacity are widespread. While machine learning (ML) approaches achieve strong predictive performance for cancer classification, algorithmic opacity and absence of interpretability frameworks tailored to resource-constrained environments have impeded clinical adoption. This study bridg…

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Machine learning identifies depression risk in older adults with chronic diseases: Clarifying shared risk factors stratified by cognitive impairment status
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Machine learning identifies depression risk in older adults with chronic diseases: Clarifying shared risk factors stratified by cognitive impairment status

Background The prevalence of depression is higher among older adults with chronic diseases and cognitive impairment than the general population. The comorbidity of cognitive impairment and chronic diseases significantly impacts the lives of these patients. This study aims to develop machine learning models to identify depression risk among older adults with chronic illnesses across different levels of cognitive impairment. Methods Data were derived from the Chinese Longitudinal Healthy Longevity Survey (n = 5798…

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Comparative quality, accuracy, and readability of large language model responses to patient questions about robotic-assisted total knee arthroplasty
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Comparative quality, accuracy, and readability of large language model responses to patient questions about robotic-assisted total knee arthroplasty

Purpose To compare the information quality, accuracy, and readability of patient-directed responses generated by large language models (LLMs), including ChatGPT-o3, ChatGPT-5.2, Gemini 3, and DeepSeek, regarding robotic-assisted total knee arthroplasty (RA-TKA). Methods Thirty frequently asked patient questions were identified using LLM outputs and Google search queries. Responses were evaluated for information quality using the DISCERN and Quality Analysis of Medical Artificial Intelligence (QAMAI) instruments,…

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Systematic Bias in Comparative Evaluations of Machine Learning Versus Logistic Regression for Clinical Prediction Models: A Meta-Research Analysis Using Trauma Mortality as an Empirical Case
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Systematic Bias in Comparative Evaluations of Machine Learning Versus Logistic Regression for Clinical Prediction Models: A Meta-Research Analysis Using Trauma Mortality as an Empirical Case

Objective Comparative evaluations of machine learning (ML) and logistic regression (LR) for clinical prediction frequently report ML as superior, but the methodological framework producing those comparisons has received limited scrutiny. We aimed to quantify the apparent discrimination advantage of ML over LR using trauma mortality prediction as an empirical case, and to characterise the evaluation practices that shape it. Study design and setting Systematic review and random-effects meta-analysis combined with…

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Predicting Conversion from Mild Cognitive Impairment to Alzheimer's Disease: A Systematic Review of Deep Learning Models for Early-Stage Disease Classification
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Predicting Conversion from Mild Cognitive Impairment to Alzheimer's Disease: A Systematic Review of Deep Learning Models for Early-Stage Disease Classification

Introduction Alzheimer's disease (AD) is a progressive neurodegenerative disorder for which early diagnosis-particularly the accurate prediction of conversion from mild cognitive impairment (MCI) to AD-is essential to enable timely and effective therapeutic interventions. Deep learning (DL) models have demonstrated substantial promise in this domain; however, critical challenges persist, including multiclass staging of disease progression, longitudinal data modeling, and effective multimodal data integration. Th…

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GENERATIVE AI FOR HIRSCHSPRUNG DISEASE: CAN SYNTHETIC FLUORESCENCE CONFOCAL MICROSCOPY IMAGES ENHANCE INTRAOPERATIVE DETECTION OF GANGLIONIC BOWEL?
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GENERATIVE AI FOR HIRSCHSPRUNG DISEASE: CAN SYNTHETIC FLUORESCENCE CONFOCAL MICROSCOPY IMAGES ENHANCE INTRAOPERATIVE DETECTION OF GANGLIONIC BOWEL?

Background Accurate identification of ganglionated bowel is essential during laparoscopic pull-through for Hirschsprung Disease (HD), yet intraoperative biopsy interpretation is time-sensitive and operator-dependent. Fluorescence confocal microscopy (FCM) provides rapid imaging of fresh tissue, and deep-learning (DL) has the potential to extract diagnostic patterns from these images automatically. However, DL development is limited by HD rarity and images scarcity. Generative-AI may address this gap by synthesiz…

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The impact of digital technology, social media, and artificial intelligence on cognitive functions: a review

In our modern society, digital devices, social media platforms, and artificial intelligence (AI) tools have become integral components of our daily lives, profoundly intertwined with our daily activities. These technologies have undoubtedly brought convenience, connectivity, and speed, making our lives easier and more efficient. However, their influence on our brain function and cognitive abilities cannot be ignored. This review aims to explore both the positive and negative impacts of these technologies on cruc…

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The impact of digital technology, social media, and artificial intelligence on cognitive functions: a review

Machine-learning prediction of urine-culture positivity in a multicentre test-ordered cohort: Model development and internal validation

Objectives This study aim to develop, compare and internally validate machine-learning models for predicting urine-culture positivity in patients who had both urinalysis and culture ordered and to explore descriptive probability strata. Post hoc secondary analyses examined age subgroups, the incremental contribution of text-derived features, simpler comparators and calibration. Patients and methods Urine culture results are typically unavailable for 24-72 h, creating uncertainty during initial assessment, and ma…

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Machine-learning prediction of urine-culture positivity in a multicentre test-ordered cohort: Model development and internal validation

FedMediFormer-XAI: Federated Multimodal Transformers with Diffusion Augmentation and Graph-Based Drug Recommendation for Diabetes

Diabetes management faces a number of obstacles, such as fragmented healthcare data, privacy concerns, poor explainability, and the lack of personalized therapeutic guidance. This research work introduces FedMediFormer-XAI, a unified and proper framework that incorporates federated learning, multimodal transformers, diffusion-based data augmentation, Graph Neural Networks (GNNs) for drug recommendation, and Explainable Artificial Intelligence (XAI) for diabetes intelligence. The system utilizes diverse healthcar…

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FedMediFormer-XAI: Federated Multimodal Transformers with Diffusion Augmentation and Graph-Based Drug Recommendation for Diabetes

Advancing preoperative planning technology in total joint arthroplasty with a real-time machine learning calculator: 1-year mortality risk in a value-based care era

Background Technological innovation in total joint arthroplasty (TJA) has largely focused on intraoperative precision through robotics, navigation, and implant design, while preoperative decision-making remains comparatively underdeveloped. Accurate estimation of patient-specific risk is central to surgical indications, yet existing tools provide limited resolution for consequential outcomes such as 1-year mortality. Methods A machine learning model was developed using the TriNetX Research Network. Patients unde…

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Advancing preoperative planning technology in total joint arthroplasty with a real-time machine learning calculator: 1-year mortality risk in a value-based care era

Machine Learning for Mortality Prediction in Infective Endocarditis: A Systematic Review and Meta-Analysis

Infective endocarditis (IE) continues to be an often fatal condition despite improvements in cardiac surgical procedures and antibiotic therapy, and conventional scoring tools show poor generalizability. Machine learning (ML) addresses these limitations by capturing complex, nonlinear clinical relationships, outperforming conventional scores in predictive accuracy, though prior ML work in IE has focused on diagnosis. A PRISMA-compliant systematic review and meta-analysis of PubMed (Supplemental Digital Content,…

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Machine Learning for Mortality Prediction in Infective Endocarditis: A Systematic Review and Meta-Analysis

Artificial intelligence for lung disease quantification in systemic sclerosis-associated interstitial lung disease and other connective tissue disease-associated interstitial lung disease

Purpose of review Systemic sclerosis-associated interstitial lung disease (SSc-ILD) is the leading cause of disease-related mortality in systemic sclerosis and the connective tissue disease-associated ILD (CTD-ILD) in which artificial intelligence imaging has advanced most rapidly. Visual high-resolution CT (HRCT) scoring is reader-dependent and limits clinical decision-making. This review summarizes clinically relevant artificial intelligence and radiomics publications from approximately the last 18 months, foc…

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Artificial intelligence for lung disease quantification in systemic sclerosis-associated interstitial lung disease and other connective tissue disease-associated interstitial lung disease

How Well Do AI Chatbots Understand Abnormal Anatomy: A Comparative Study Using Congenital Anomalies and Tumor Cases

Artificial Intelligence (AI) chatbots are becoming an efficient option to understand medical data and also help with clinical reasoning. There has been a recent progression in research of large language models and their ability to be used in the healthcare sector, such as radiological image analysis, and diagnostic support. There is however, little evidence supporting their ability to accurately understand abnormal anatomical conditions, such as congenital anomalies and tumor related changes in anatomy. To asses…

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How Well Do AI Chatbots Understand Abnormal Anatomy: A Comparative Study Using Congenital Anomalies and Tumor Cases

Stakeholder perspectives on artificial intelligence in schizophrenia care

Background Artificial intelligence (AI) is explored as a tool to expand access to mental health care, offering support and self-disclosure. To date, the perspectives of individuals with schizophrenia on AI have been absent from the medical literature. This exploratory study represents an effort to report schizophrenia patient perspectives on AI chatbots through a focus group. Methods We conducted an exploratory, cross-sectional, qualitative study using semi-structured focus groups at a large academic health cent…

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Stakeholder perspectives on artificial intelligence in schizophrenia care