The era of predictive modeling enhanced with machine learning and artificial intelligence (AI) to aid clinical ...

Assessing the progress of new AI language models can be as challenging as training them. Stanford researchers offer a new approach.

With EAGLE, lung cancer biopsy analysis is expedited, accurately predicting EGFR mutations and streamlining the diagnostic process for better patient outcomes.

Learn how Kimi K2’s open-weight framework and sparse architecture are transforming AI coding and fostering global ...

Whole-mount 3D imaging at the cellular scale is a powerful tool for exploring complex processes during morphogenesis. In organoids, it allows examining tissue architecture, cell types, and morphology ...

Isolation Forest detects anomalies by isolating observations. It builds binary trees (called iTrees) by recursively ...

Liver cancer is the sixth most common cancer globally and a leading cause of cancer-related deaths. Accurate segmentation of liver tumors is a crucial step for the management of the disease, but ...

The growing contacts between people and Indian rock reptiles, especially Russell's Viper and Python, have made precise and early identification more important to guarantee public safety and support of ...

Boltz-2 is the first biomolecular co-folding model to combine structure and binding affinity prediction, approaching the accuracy of physics-based free energy perturbation (FEP) calculations but ...

Logistic regression is the most cost-effective model for medial vascular calcification classification, with a mean ICER of $278 using five low-cost features. Despite similar diagnostic accuracy ...

Comparative diagnostic accuracy of deep learning and hand-crafted radiomics models for detecting lymph node metastases in head and neck cancers: A meta-analysis.. If you have the appropriate software ...

Home > Computing Microsoft's AI Weather Model Is More Accurate, Less Expensive Than Traditional Forecasting That's according to a research paper written largely by Microsoft employees, at least.