| tags: | |
| - schemaforge | |
| - ai-refined | |
| - machine-learning-research | |
| license: mit | |
| # www.nature.com | |
| Auto-refined by [SchemaForge](https://schemaforge.duckdns.org) | |
| ## Metadata | |
| - **Topic:** Machine Learning Research | |
| - **Quality Score:** 0.95 | |
| - **Source:** Autonomous web scraper | |
| ## Extracted Facts | |
| - Machine learning is the ability of a machine to improve its performance based on previous results. | |
| - Machine learning methods enable computers to learn without being explicitly programmed. | |
| - Privacy risks from medical AI tools are not shared equally. | |
| - Learning from routine health system data builds better neuroimaging AI models. | |
| - A new method designs RNA sequences by learning from alignments of structurally similar molecules. | |
| - MRICombo: a deep-learning-based framework for universal volumetric segmentation grading-staging and malignancy detection across heterogeneous MRI. | |
| - AI-based augmentation of oncology clinical trials. | |
| - The Virtual Tissues foundation model resolves spatial proteomics across scales. | |
| - Divergent impacts of explainable AI for dermatological diagnosis on clinicians versus lay people. | |
| - Automatic report-based assessment of radiology-pathology concordance in surgical patients using BERT and DPCNN. | |
| - AI agents are checking the scientific literature — and spotting decades-old errors. | |
| - Privacy attacks can reveal whether someone’s medical data was used to train an AI model. | |