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877 published stories · page 22 of 59

Evidence-backed gain

Generative artificial intelligence, human creativity, and art

Recent artificial intelligence (AI) tools have demonstrated the ability to produce outputs traditionally considered creative. One such system is text-to-image generative AI (e.g. Midjourney, Stable Diffusion, DALL-E), which automates humans' artistic execution to generate digital artworks. Utilizing a dataset of over 4 million artworks from more than 50,000 unique users, our research shows that over time, text-to-image AI significantly enhances human creative productivity by 25% and increases the value as measur…

PNAS Nexus · Media & Arts

Generative artificial intelligence, human creativity, and art

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…

Health

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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[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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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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Evidence-backed gain

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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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

IFPI Backs AI Music Labeling, Implements Change on Global Charts

The IFPI has implemented labeling for music created with generative artificial intelligence on a number of global charts.

Media & Arts

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

Adaptive level modification via player skill classification and large language models

Maintaining player engagement in video games requires a careful balance between challenge and player competence. Static difficulty settings fail to account for individual skill variation, while existing dynamic difficulty adjustment systems are limited to tuning low-level game parameters rather than restructuring level content. This paper presents an adaptive level modification framework that personalizes gameplay by continuously inferring player skill and applying targeted structural modifications to level cont…

Science

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

Metabolites stratify future major depressive disorder risk in obese population: a longitudinal machine learning analysis

Background Obesity is a well-established risk factor for major depressive disorder (MDD), yet the risk is not uniform, highlighting the need for precise risk stratification. This study aimed to develop a metabolomics-based prediction model to identify high-risk metabolic phenotypes among obese participants and to elucidate the causal metabolic pathways involved. Methods Forty-one-thousand-four-hundred-fifty-nine obese participants were followed for a median of 14.4 years. We integrated multiple machine learning…

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