Bridging online behavior and clinical insight: A longitudinal LLM-based study of suicidality on youtube reveals novel digital markers.
Longitudinal study links YouTube behavior to suicidality, revealing 166 topics and key digital markers. 📊💔
An AI-based algorithm for analyzing physical activity and health-related fitness in youth.
AI-driven analysis boosts youth fitness assessments: 98.448% accuracy in classification and enhanced performance predictions. 📊🤖
Enhancing Developmental Language Disorder Identification with Artificial Intelligence: Development of an Explainable Screening App Using Real and Synthetic Data.
AI-driven app enhances DLD identification, showing high concordance with clinical diagnoses. Key linguistic markers analyzed. 📊🤖
Leading large language models on a periodontology knowledge test.
Large language models show 65% accuracy in periodontology tests, lacking reliability for clinical decision support. 📊🦷
Data Science Education for Residents, Researchers, and Students in Psychiatry and Psychology: Program Development and Evaluation Study.
AI in Psychiatry Training: 10 trainees improved NLP confidence from 1.35 to 2.79! 📈💻 #DataScienceEducation
![[Responsible use of AI and robotics in medicine : Opportunities, challenges and perspectives for otorhinolaryngology].](https://yesilscience.com/wp-content/uploads/2026/01/Responsible-use-of-AI-and-robotics-in-medicine-Opportunities-challenges-and-perspectives-for-otorhinolaryngology.jpg)
[Responsible use of AI and robotics in medicine : Opportunities, challenges and perspectives for otorhinolaryngology].
AI and robotics in medicine: Transforming practices with ethical challenges. Key principles: transparency, shared responsibility, legitimacy. 🤖⚕️
Effects of Artificial Intelligence Recognition-Based Telerehabilitation on Exercise Capacity in Patients With Hypertension: Randomized Controlled Trial.
AI telerehabilitation boosts exercise capacity in hypertension patients: 62.77m improvement in 6MWD after 8 weeks! 📈💪
Artificial intelligence-driven clustering for phenotyping life-threatening prehospital trauma.
AI clusters trauma patients into three phenotypes, revealing 93.1% mortality in T-1 group. Key for emergency care! 🚑📊
Scoping review of regulatory transparency in AI-based radiology software: analysis of PMDA-approved SaMD products.
Exploring AI in radiology: 2026 review on PMDA-approved software reveals transparency gaps. 📊🤖
AI-powered hierarchical classification of ampullary neoplasms: a deep learning approach using white-light and narrow-band imaging.
AI-driven deep learning enhances ampullary lesion diagnosis: 92.2% accuracy, 83.3% sensitivity for high-grade dysplasia. 📊🔍