Explainable AI Spots Brain Tumors on MRI Scans in Milliseconds
Researchers in Bangladesh have developed an explainable deep learning framework that classifies brain tumors on MRI scans in 2.45 milliseconds ...
Researchers in Bangladesh have developed an explainable deep learning framework that classifies brain tumors on MRI scans in 2.45 milliseconds ...
Researchers in Shanghai have built a privacy-preserving federated AI system that fuses bedside ventilator, ultrasound, and blood gas data with ...
A modular study of multimodal AI shows audiologist-guided prompts dramatically improve hearing-test interpretation, while no-response handling and cross-model reliability remain ...
Researchers have built a federated deep learning system that predicts sepsis across simulated hospitals with 93.6 percent accuracy while keeping ...
Researchers in Qingdao developed an interpretable XGBoost model that distinguishes gastric cancer from intestinal metaplasia in patients with psychological symptoms ...
University of Washington researchers have built a decision support system that combines PET imaging, immune repertoire sequencing, and blood cytokines ...
A multimodal deep learning model combining histopathological images with clinical data achieved 97.17 percent accuracy in stratifying colorectal cancer patients ...
Researchers in India report that a fuzzy logic-driven dynamic clustering algorithm outperformed standard machine learning methods on brain tumor data, ...
Researchers in China have developed MKMed, a cross-modal AI framework that aligns five types of drug knowledge to overcome the ...
A study of nearly 12,000 patients found that leading large language models matched or exceeded an intermediate ophthalmologist in recommending ...
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© 2026 Scienmag - Science Magazine