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| tags: |
| - schemaforge |
| - ai-refined |
| - machine-learning-research |
| license: mit |
| --- |
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| # www.nature.com |
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| Auto-refined by [SchemaForge](https://schemaforge.duckdns.org) |
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| ## Metadata |
| - **Topic:** Machine Learning Research |
| - **Quality Score:** 0.95 |
| - **Source:** Autonomous web scraper |
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| ## 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. |
| - Machine learning-based classification of diabetes mellitus using sociodemographic, behavioral, and clinical predictor. |
| - 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. |
| - Inference of tumor spatial habitats. |
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