The cost of explainability in artificial intelligence-enhanced electrocardiogram models.
AI-ECG models: Balancing performance and explainability. 📊🔍 Study reveals trade-offs in interpretability and diagnostic accuracy.
The global epidemiology of acute kidney injury: challenges and opportunities.
Global AKI: 2 million deaths annually, rising chronic kidney disease risk, and urgent need for equitable care. 🌍💔
A combined machine learning and finite element modelling tool for the surgical planning of craniosynostosis correction.
Innovative tool enhances craniosynostosis surgery planning with 0.95 accuracy! 🤖🧠 Reduces radiation exposure, improves outcomes.
Concordance among experts in assessing apical mucosal preservation during holmium laser enucleation of the prostate (HoLEP): implications for artificial intelligence model development.
Expert consensus on apical mucosal preservation in HoLEP shows 85% agreement, crucial for AI model accuracy. 🤖🔍
Enhancing home rehabilitation through AI-driven virtual assistants: a narrative review.
AI-driven virtual physiotherapy assistants enhance home rehabilitation, improving adherence and outcomes. Key challenges include sensor accuracy and engagement. 🤖🏡
Quantitative Imaging for Interstitial Lung Disease.
Quantitative imaging enhances ILD diagnosis and treatment, utilizing CT and emerging MRI techniques. 📊🫁
Are we entering a new era of artificial intelligence (AI)-supported decision-making in dentistry?
Exploring AI's role in dentistry: potential impacts on decision-making and patient outcomes. 🤖🦷
Foundation model based prediction of lung cancer survival using temporal changes in dual time point CT scans.
Lung cancer survival prediction enhanced by dual time point CT scans. 📊 Study shows improved accuracy using foundation models. 🩺
Maternal lipidomic signatures of preterm and small-for-gestational-age newborn infants in low- and middle-income countries.
Maternal lipid levels impact preterm and SGA births. Key findings: triglyceride imbalances, AUC 0.69 for preterm prediction. 📊👶
Immunological risk factors for recurrent implantation failure using a deep learning model: a multicenter retrospective cohort study.
Deep learning predicts live births in recurrent implantation failure with 87.4% accuracy. Key factors: age, Th1/Th2 ratio, BMI. 📊🤰