aceEvidence-backed problem
Suno Held Liable in GEMA Lawsuit for Infringing German Song Copyrights
A German court says Suno illegally trained on unlicensed songs, one of the first major legal blows to an AI music company in copyright litigation.
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1046 published stories · page 33 of 70
Evidence-backed problem
The major labels propose rules to keep AI slop off the charts
The major labels have proposed rules that would keep most AI music off the charts unless it was ‘substantially human-made.’
Evidence-backed problem
Blood Rain music video roasted for being "AI slop"
Shift Up released a music video for Stellar Blade: Blood Rain, and fans are calling it out for being AI-generated (and also bad).
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.
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…
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…
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,…
[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…
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…
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…
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…
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.
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…
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…
