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Machine Learning-Based Prediction of Poor Outcomes in Intracerebral Hemorrhage: A Systematic Review and Meta-Analysis

Spontaneous intracerebral hemorrhage (ICH) is associated with high risks of mortality and disability, yet early and accurate outcome prediction remains challenging. This study systematically evaluated the performance of machine learning (ML) models in predicting key adverse outcomes (hematoma expansion [HE], poor functional outcome, mortality) in ICH, aiming to provide consolidated evidence for future research and clinical translation. We systematically searched PubMed, Embase, Web of Science, and Cochrane Libra…

Brain and Behavior · Health

Machine Learning-Based Prediction of Poor Outcomes in Intracerebral Hemorrhage: A Systematic Review and Meta-Analysis

Evidence-backed gain

Classification of tau status with machine learning models in amyloid-positive cohorts

Although tau positron emission tomography (PET) imaging is effective for staging tau pathology, it is limited clinically by cost and availability. Machine learning models based on magnetic resonance imaging (MRI)- and amyloid PET-derived features may serve as useful screening tools for tau pathology. Multiple machine learning models were developed to classify tau positivity in the Braak III/IV region using structural MRI, amyloid PET, and demographic features. Alzheimer's Disease Neuroimaging Initiative (ADNI) (…

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Evidence-backed problem

Why bad health information can seem so convincing-and five ways to resist it

Recent reports have highlighted AI-generated "doctors" spreading dubious health advice to millions of social media users. Fabricated wellness trends can travel just as quickly. A supposed "pink jelly" weight-loss treatment, for example, reportedly fooled at least one celebrity.

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Evidence-backed gain

Black people in the US: have you used AI for health-related questions?

Artificial intelligence is growing in influence in the healthcare space, and more people are seeking guidance from AI tools, including AI-generated social media influencers. Black people in the US, who have been long subject to racism in the healthcare system, are among some of those who are leaning on AI for health-related matters and information. We would like to hear from Black respondents about their experiences with Black AI-generated health and spiritual influencers.

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Evidence-backed gain

Imminent opioid overdose risk prediction using classical and machine learning survival models following first recorded opioid-related diagnosis: a prospective cohort study from the <i>All of Us</i> research program

Background: Identifying patients at risk of opioid overdose in healthcare settings is critical, yet evidence on predictive models and their performance to predict imminent opioid overdose remains limited. Objective: We compared classical and Machine Learning (ML) survival models to predict 30-day overdose risk following a first opioid-related diagnosis to determine whether algorithmic complexity improves clinical decision support. Methods: We conducted a prospective cohort study using longitudinal Electronic Hea…

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Evidence-backed gain

Educational Artificial Intelligence Software to Support Assessment of Atopic Dermatitis Severity

Accurate assessment of the severity of atopic dermatitis is crucial for guiding treatment. However, the Eczema Area and Severity Index (EASI), a widely used clinical assessment tool, relies on subjective visual scoring, which can lead to inter-rater variability. This study aimed to evaluate the performance of convolutional neural network-based artificial intelligence software in assessing atopic dermatitis severity from uncropped, unmasked skin images acquired under uncontrolled conditions. In this prospective,…

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Both readings

[Use of artificial intelligence in clinical practice and hospitals]

Artificial intelligence (AI) is increasingly evolving from a research technology into a tool for everyday clinical practice. While early applications primarily focused on medical image analysis, generative AI systems and large language models are now available for a wide range of clinical and administrative tasks. These include medical documentation, literature review, guideline-based knowledge management, patient communication, and workflow optimization. At the same time, diagnostic and therapeutic applications…

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Development and external validation of a machine learning model for predicting postoperative hydrocephalus in 1,073 posterior fossa tumor patients

Postoperative hydrocephalus is a common complication following posterior fossa tumor resection, affecting 7-40% of patients. Although preoperative cerebrospinal fluid (CSF) diversion may be required in selected patients with hydrocephalus, decisions remain individualized in routine neurosurgical practice. We therefore developed and externally validated a model to provide supplementary preoperative risk stratification using routinely available variables. We retrospectively analyzed 1,073 patients following resect…

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Development and external validation of a machine learning model for predicting postoperative hydrocephalus in 1,073 posterior fossa tumor patients

Innovative approaches to therapeutic target discovery amid the global challenge of antimicrobial resistance

Antimicrobial resistance is a constant threat to global public health, requiring innovative strategies for therapeutic target identification. Hence, this narrative review discusses the application of structural modeling and artificial intelligence in the functional prediction of proteins encoded by multidrug-resistant bacterial genomes. Tools such as AlphaFold and RoseTTAFold have enabled high-accuracy three-dimensional structure prediction, facilitating the annotation of hypothetical proteins and the identifica…

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Innovative approaches to therapeutic target discovery amid the global challenge of antimicrobial resistance

Quorum-sensing, microbiome interactions, and emerging artificial intelligence-assisted anti-virulence strategies in Salmonella Typhi: a critical review of translational opportunities and challenges

Typhoid fever, caused by Salmonella enterica subsp. enterica serovar Typhi (Salmonella Typhi), remains a significant global health challenge that is increasingly complicated by the emergence and spread of multidrug-resistant (MDR) and extensively drug-resistant strains. Growing limitations of antibiotic-centered treatment strategies have stimulated interest in anti-virulence approaches targeting bacterial regulatory networks rather than viability alone. Among these, quorum-sensing (QS), particularly the LuxS-med…

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Quorum-sensing, microbiome interactions, and emerging artificial intelligence-assisted anti-virulence strategies in Salmonella Typhi: a critical review of translational opportunities and challenges

Interpretable Machine Learning for Population-Level Tooth Loss Prediction

Machine learning can support population-level severe tooth loss (STL; ≥6 missing teeth) risk stratification; however, a lack of calibration under domain shift, limited interpretability of conventional black-box models, and inadequate handling of complex survey designs constrain responsible public health interpretation and implementation. We implemented and evaluated an interpretable, survey-weighted Multiple Imputation by Chained Equations-Explainable Boosting Machine (MICE-EBM) framework for population-level ST…

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Interpretable Machine Learning for Population-Level Tooth Loss Prediction

Automated classification of dental implant brands and prosthetic platform sizes on panoramic radiographs using deep learning

Statement of problem Incomplete clinical records can be a significant hurdle in implant dentistry, transforming routine maintenance or a restorative task into a complex search. When the primary documentation-such as the implant passport or surgical report-is missing, the clinician is forced to rely on radiographic identification and trial-and-error, which increases the risk of component mismatch and patient dissatisfaction. Purpose The purpose of this study was to develop and validate an artificial intelligence…

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Automated classification of dental implant brands and prosthetic platform sizes on panoramic radiographs using deep learning

Identification of Methionine Metabolism-Driven Heterogeneous Subtypes in Colorectal Cancer and Their Associated Immune Microenvironment

Aim Tumor heterogeneity, driven by metabolic reprogramming, challenges colorectal cancer (CRC) treatment. Methionine metabolism is crucial for tumor progression, but its role in CRC heterogeneity and the tumor immune microenvironment (TIME) requires systematic investigation. Methods A systematic evaluation of 101 combinations of machine learning and statistical algorithms was conducted within a 10-fold cross-validation framework to develop and validate the optimal model, termed the methionine metabolism-related…

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Deep learning models for predicting clinical activity and characteristics in thyroid eye disease using magnetic resonance imaging

Purpose This project aims to develop and evaluate deep learning models using orbital magnetic resonance imaging for the prediction of continuous clinical activity score and key patient characteristics in thyroid eye disease. Methods The publicly available TOM500 dataset, consisting of orbital magnetic resonance imaging scans and clinical data from 500 thyroid eye disease patients, was split into training ( n = 360), validation ( n = 100), and test ( n = 40) sets. A ResNet-50 convolutional neural network pretrain…

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Deep learning models for predicting clinical activity and characteristics in thyroid eye disease using magnetic resonance imaging

Machine learning approaches to predict early cardiac immune-related adverse events in patients receiving immune checkpoint inhibitors

Purpose Immune checkpoint inhibitor (ICI)-induced cardiac immune-related adverse events (cardiac irAEs) are rare yet serious complications. Clinical assessment tools to identify at-risk patients would allow for more effective prevention strategies, thus improving clinical outcomes. We constructed various machine learning (ML) models to predict these events among patients receiving ICI therapy. Methods A cohort of patients receiving ICI therapy from 2010 to 2023 was identified from the TriNetX database. Cardiac i…

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Machine learning approaches to predict early cardiac immune-related adverse events in patients receiving immune checkpoint inhibitors