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. 📊🤰
Evaluating EEG-to-text models through noise-based performance analysis.
EEG-to-text models show potential but may memorize patterns instead of learning. Rigorous evaluation needed! 🧠📊
Mapping war trauma: A machine learning approach to predict mental health impacts in Ukraine.
Machine learning predicts mental health impacts in Ukraine's war, revealing key drivers of PTSD and anxiety. 📊🧠
Identifying Key Variances in Clinical Pathways Associated With Prolonged Hospital Stays Using Machine Learning and ePath Real-World Data: Model Development and Validation Study.
Machine learning identifies clinical variances linked to prolonged hospital stays, enhancing patient management. 📊🏥
Radiographic diagnosis of periodontitis using artificial intelligence: a meta-analysis comparing binary and staging classifications across imaging modalities.
AI in Periodontitis Diagnosis: Sensitivity 87.2% 📊, Accuracy 88.9% 🦷—A Meta-Analysis Review of Imaging Modalities.