Systematic scoping review of external validation studies of AI pathology models for lung cancer diagnosis.
AI models for lung cancer diagnosis show AUC values from 0.746 to 0.999, but require more robust external validation. ππ©Ί
June 8, 2025
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Discover the newest research about AI innovations in π€ Machine Learning.
A deep learning system for detecting silent brain infarction and predicting stroke risk.
Deep learning detects silent brain infarctions via retinal images, achieving AUCs of 0.901 for incident and 0.769 for recurrent strokes. π§ π
June 8, 2025
Histopathologic deep learning model for predicting tumor response to hepatic arterial infusion chemotherapy plus TKIs and ICIs in large hepatocellular carcinoma.
Deep learning predicts tumor response in large HCC: HTI shows 54.45% ORR vs. 21.65% for HAIC. ππ§¬
June 8, 2025
The Integration of Artificial Intelligence and Augmented Reality in Dental Education: Current Applications and Future Potential.
AI and AR in dental education enhance skills, improve diagnostics, and offer immersive learning experiences. Significant potential ahead! π¦·π€
June 7, 2025
RadNet Expands AI Capabilities with Acquisition of See-Mode Technologies
RadNet has acquired See-Mode Technologies to enhance AI capabilities in ultrasound imaging for thyroid and breast health. ππ©Ί
June 7, 2025
Letter to the Editor on “Artificial Intelligence Based Augmented Reality Navigation in Minimally Invasive Partial Nephrectomy”.
Exploring AI in minimally invasive nephrectomy: insights from Guo Q’s letter on augmented reality navigation. π€π
June 7, 2025
Collaborative Efforts of Health Centers to Address AI Implementation Challenges
Health centers collaborate to tackle AI challenges, enhancing care access and operational efficiency. π€π‘
June 7, 2025
Speaking from the heart: a story about innovation, resilience, and infinite possibilities with AAC.
Exploring AAC innovation: emotional expression, AI integration, and brain-computer interfaces transform communication. π±π¬
June 7, 2025
Comparing models and experimental structures of the GPR101 receptor: Artificial intelligence yields highly accurate models.
AI models outperform traditional methods in GPR101 receptor analysis, revealing critical insights for GPCR modeling. π€π¬
June 6, 2025
Subgrouping autism and ADHD based on structural MRI population modelling centiles.
“Exploring distinct neuroanatomical subgroups in autism and ADHD through advanced MRI population modeling. π§ π”
June 5, 2025